Cybersecurity and Evolutionary Data Engineering: Select Proceedings of the 2nd International Conference, ICCEDE 2022 (Lecture Notes in Electrical Engineering, 1073) 9819950791, 9789819950799

Table of contents :
Organizations
Preface
Keynotes
Contents
Editors and Contributors
Cybersecurity and Digital Forensic
Current Status of Challenges in Data Security: A Review
1 Introduction
2 Literature Survey
3 Data Mining Techniques
4 Data Masking Techniques
5 Challenges in Data Security
6 Importance of Security and Privacy
7 Conclusion
References
Cyber Bullying: The Growing Menace in Cyber Space with Its Challenges and Solutions
1 Introduction
1.1 Definitions of Bullying
1.2 Types of Bullying
2 Bullying on the Internet
3 Literature Review over Cyber Bullying
4 Governmental Efforts and Legislation Provision in Indian Perspective
4.1 The Nirbhaya Funds Scheme
4.2 Cybercrime Prevention Against Women and Children Scheme (CCPWC Scheme)
4.3 Indian Cybercrime Coordination Center (I4C) Scheme
4.4 The National Cyber Crime Reporting Portal (NCCR Portal)
5 Findings of the Survey
6 Conclusion and Future Scope
References
Hybrid Feature Extraction for Analysis of Network System Security—IDS
1 Introduction
2 Literature Survey
3 System Model
3.1 Feature Normalization
3.2 Feature Selection
4 Performance Evaluation
5 Conclusion
References
Stranger Trust Architecture: An Advancement to Zero Trust Architecture
1 Why a New Method?
2 Previous Works
3 Basics of Stranger Trust Model
4 Tenets of Stranger Trust
5 STA Features
5.1 Multi User Authentication
6 Conclusion
References
Genetic Algorithm Optimized SVM for DoS Attack Detection in VANETs
1 Introduction
2 Literature Review
3 Problem Statement: DoS Attack
3.1 Overwhelming the Node’s Resources
3.2 Channel Jamming
4 Dataset
5 Proposed System
5.1 Data Pre-processing
5.2 Feature Selection Using Genetic Algorithm
5.3 Fitness Function
6 Results
7 Conclusion and Future Work
References
Digital and IoT Forensic: Recent Trends, Methods and Challenges
1 Introduction
2 Forensics
3 Digital Forensics
3.1 Steps Involved in Digital Forensics Investigation
3.2 Current Digital Forensics Trends
3.3 Classification of Digital Forensics Concerns
4 IoT Forensic
4.1 Steps Involved in IoT Forensics
4.2 IoT Security Challenges
5 Conclusion
References
Cloud-Based Occlusion Aware Intrusion Detection System
1 Introduction
2 Proposed Work
2.1 Face Detection
2.2 Face Recognition
2.3 Deployment on Google Cloud
3 Experimental Results
4 Conclusion
References
Comparative Analysis of Web Application Based Encryption Methods
1 Introduction
2 Basic Theory and Methodologies
2.1 Login System
2.2 Encryption Method
2.3 Types of Encryptions
2.4 Generation of SHA512 Digest
3 Proposed Method
3.1 Vulnerability Analysis
3.2 Analysis for Needs and Betterment
3.3 Mitigate and Test
4 Result and Discussion
5 Conclusion
References
Linking of Ontologies for Composition of Semantic Web Services Using Knowledge Graph
1 Introduction
1.1 Web Services and Its Requirements
1.2 Semantic Web Services and Its Framework
2 Literature Review
3 Research Questions
4 Research Methodology
4.1 Preprocessing Module
4.2 Creation of Knowledge Graph (KG)
4.3 Discovery Phase
5 Results and Analysis
5.1 Reasoning Over KG of OWL-S Services
6 Conclusion and Future Scope
References
Fake Image Dataset Generation of Sign Language Using GAN
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Collection
3.2 Pre-Processing and Heat Map
3.3 GAN Algorithm
3.4 Fake Image Dataset
4 Proposed Model
4.1 Algorithm of Proposed Model
4.2 Working of Proposed Model
5 Experiment Setup
5.1 Dataset Description
5.2 Experimental Testbed
6 Results
7 Conclusion and Future Work
References
Analysis of Multimodal Biometric System Based on ECG Biometrics
1 Introduction
2 ECG as Biometrics
2.1 ECG Biometrics Merits and Prime Challenges
2.2 Physiology of the ECG
2.3 ECG Datasets
3 Unimodal and Multimodal Biometrics
4 Multimodal Biometrics (Two Modalities)
5 Multimodal Biometrics with ECG (Two Modalities)
6 Multimodal Biometrics with ECG (Three Modalities)
7 Conclusion and Future Scope ECG Based Multimodal
References
Performance Analysis of Nature Inspired Optimization Based Watermarking Schemes
1 Introduction
2 Nature-Inspired Algorithms (NIAs) for Optimal Watermarking
2.1 Particle Swarm Optimization
2.2 Artificial Bee Colony (ABC)
2.3 Firefly Algorithm (FA)
3 Conclusion
References
Evolutionary Data Engineering Applications
A Review of Ensemble Methods Used in AI Applications
1 Introduction
2 Related Work
2.1 Bias-Variance Decomposition
2.2 Diversity
3 Ensemble Strategies
3.1 Bagging
3.2 Boosting
3.3 Stacking
3.4 Deep Ensemble Methods Use Negative Correlation
3.5 Explicit or Implicit Ensembles
3.6 Homogeneous or Heterogeneous Ensembles
4 Application of Ensemble Learning Methods
5 Conclusion and Future Scope
References
Stress Detection Based on Multimodal Data in a Classroom Environment
1 Introduction
2 Related Work
3 Methodology
3.1 Architecture
3.2 Module Description
4 Results
5 Conclusions and Future Work
References
Comparative Study of Different Generations of Mobile Network
1 Introduction
1.1 First Generation (1G)
1.2 Second Generation(2G)
1.3 Third Generation (3G)
1.4 Fourth Generation (4G)
1.5 Fifth Generation (5G)
1.6 Sixth Generation (6G)
2 Different Mobile Generation
3 Discussion and Future Scope
4 Conclusion
References
A Novel Framework for VM Selection and Placement in Cloud Environment
1 Introduction
2 Related Work
3 Proposed Work
4 Conclusion
References
Quad Clustering Analysis and Energy Efficiency Evaluation in Wireless Sensor Networks
1 Introduction
2 Literature Review
3 Quad Clustering
3.1 Quad Clusters Formation
3.2 Energy Formula
4 Quad Clusters Analysis and Performance Evaluation
4.1 Simulation Environment
4.2 Cluster Analysis
5 Conclusion
References
Artificial Intelligence in Gaming
1 Introduction
2 Artificial Intelligence
3 Review of Literature
4 Comparison
5 Conclusion
References
Analysis of Pulmonary Fibrosis Progression Using Machine Learning Approaches
1 Introduction
1.1 Basic Details
1.2 How Pulmonary Fibrosis is Diagnosed?
1.3 Different Procedures for Finding the Lung Tissue
2 Literature Review
3 Methodology
3.1 Formulation of FVC Slope
4 Result
5 Conclusion
6 Future Scope
References
Lung Conditions Prognosis Using CNN Model
1 Introduction
2 Research Objective
3 Methodology
4 Algorithm Working
5 Result Analysis
5.1 Dataset
5.2 Experiment Analysis
6 Conclusion
References
Stock Trend Prediction Using Candlestick Pattern
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Collection
3.2 Date Pre-processing
3.3 Candlestick Identification Algorithm
3.4 Signal Generation
4 Proposed Model
4.1 Candlestick Patterns
5 Experimental Setup
5.1 Experiment Testbed
5.2 Dataset Description
6 Result
7 Conclusion and Future Work
References
Motion Based Real-Time Siamese Multiple Object Tracker Model
1 Introduction
2 Motion-Based Target Detection
3 Proposed Problem Identification
4 Literature Review
5 Proposed Methodology
5.1 Proposed Algorithm
5.2 Images and Date Set
6 Results
7 Discussion
8 Conclusion
9 Future Scopes
References
Design and Development of IOT & AI Enabled Smart Entrance Monitoring Device
1 Introduction
2 Related Work
3 Software and Hardware Specifications
3.1 Software Specifications
3.2 Hardware Specifications
4 Methodology
4.1 Face Mask Identification
4.2 Temperature Measuring
5 System Overview
6 Results and Discussion
7 Conclusion
References
Sunlight-based Framework: An Approach for Energy Efficiency in IoT Systems
1 Introduction
2 Need for Sunlight-Based Framework
3 Literature Review
4 Enabling Techniques
4.1 Temperature Sensors
4.2 Moisture Sensors
4.3 Light Sensors
4.4 Motion Sensors
4.5 Proximity Sensor
4.6 Node MCU
4.7 IR Sensors
4.8 Current Sensor
4.9 Solar Panel
5 Research Methodology
6 Proposed Model
7 Conclusion and Future Work
References
Metaverse Technologies and Applications: A Study
1 Introduction
1.1 Metaverse
1.2 Framework of Metaverse
1.3 Need for Metaverse
2 Literature Review
3 Technologies related to Metaverse
3.1 Augmented Reality and Virtual Reality
3.2 Artificial Intelligence
3.3 3-D Modelling
3.4 Edge Computing
3.5 Internet of Things (IoT)
4 Application Domains and Use Cases of the Metaverse
4.1 Advanced Blockchain Use Cases
4.2 Virtual Work and Learning Spaces
4.3 Virtual Business and Markets
4.4 Expansion in Social Media Platforms
5 Challenges of Metaverse Implementations
5.1 Ethical Issues
5.2 Security
5.3 Currency and Digital Payments
5.4 User Acceptance
6 Conclusion and Future Scope
References
Body Sensor Networking: A Case Study of Heart Pulse Detection and Alert System
1 Introduction
2 Technologies Used in BSN
3 Architecture of BSN
4 Proposed System to Detect Heartbeat and Generate Alerts
5 Challenges of BSN
5.1 Reliability
5.2 Privacy and Security
5.3 Portability
5.4 Energy Consumption
5.5 Sensitivity
5.6 Effect of Electronics on Human Body
6 Recent Research, Current Applications, and Use of Bsn in Different Industries
7 Conclusion
References
Brand Sentiment Analytics Using Flume
1 Introduction
2 MapReduce in Hadoop
3 SQOOP
3.1 Features And Limitations Of SQOOP
4 Hive
5 Conclusion
References
The ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (MathID2) /StPNE pdfmark [/StBMC pdfmarkto.ps: [/EMC pdfmark [/Artifact /BDC pdfmark λps: [/EMC pdfmark [/StBMC pdfmark ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmarkNF Problem
1 Introduction
1.1 Naïve Solution
1.2 Hashing
1.3 Dijkstra's Solution
2 The Indian National Flag Problem
3 The Zimbabwean National Flag Problem
4 The National Flag with ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (MathID65) /StPNE pdfmark [/StBMC pdfmarkto.ps: [/EMC pdfmark [/Artifact /BDC pdfmark λps: [/EMC pdfmark [/StBMC pdfmark ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark Colours
5 Conclusion
References
The Indian Search Algorithm
1 Introduction
2 Indo-Pellian Search
3 Indo-Fibonaccian Search
4 Conclusion
References
COVID-19's Influence on Buyers and Businesses
1 Introduction
2 Literature Review
3 Choice of Covariates and Dimensionality Reduction
3.1 Choice of Covariates
3.2 Dimensionality Reduction
4 Time Series Forecast for Economic Factors in Selected Regions
4.1 ARIMA Model
5 Model Training
6 Implementation
7 Results
8 Conclusion
8.1 Future Work
References
Exploring Textural Behavior of Novel Coronavirus (SARS–CoV-2) Through UV Microscope Images
1 Introduction
2 Background Information About the Novel Coronavirus
3 Information About Texture Parameters of the “Grey Level Co-Occurrence Matrix”
4 Related Work
5 Experimental Results
6 Conclusion
References

Citation preview

Lecture Notes in Electrical Engineering 1073

Raj Jain Carlos M. Travieso Sanjeev Kumar   Editors

Cybersecurity and Evolutionary Data Engineering Select Proceedings of the 2nd International Conference, ICCEDE 2022

Lecture Notes in Electrical Engineering Volume 1073

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Napoli, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, München, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, University of Karlsruhe (TH) IAIM, Karlsruhe, Baden-Württemberg, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Dipartimento di Ingegneria dell’Informazione, Sede Scientifica Università degli Studi di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Intelligent Systems Laboratory, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, Department of Mechatronics Engineering, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Intrinsic Innovation, Mountain View, CA, USA Yong Li, College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, China Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Subhas Mukhopadhyay, School of Engineering, Macquarie University, NSW, Australia Cun-Zheng Ning, Department of Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Department of Intelligence Science and Technology, Kyoto University, Kyoto, Japan Luca Oneto, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Genova, Italy Bijaya Ketan Panigrahi, Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Federica Pascucci, Department di Ingegneria, Università degli Studi Roma Tre, Roma, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, University of Stuttgart, Stuttgart, Germany Germano Veiga, FEUP Campus, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Haidian District Beijing, China Walter Zamboni, Department of Computer Engineering, Electrical Engineering and Applied Mathematics, DIEM—Università degli studi di Salerno, Fisciano, Salerno, Italy Junjie James Zhang, Charlotte, NC, USA Kay Chen Tan, Department of Computing, Hong Kong Polytechnic University, Kowloon Tong, Hong Kong

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Raj Jain · Carlos M. Travieso · Sanjeev Kumar Editors

Cybersecurity and Evolutionary Data Engineering Select Proceedings of the 2nd International Conference, ICCEDE 2022

Editors Raj Jain Department of Computer Science and Engineering Washington University in St. Louis Pullman, WA, USA

Carlos M. Travieso University of Las Palmas de Gran Canaria Las Palmas, Spain

Sanjeev Kumar GL Bajaj Institute of Technology and Management Greater Noida, Uttar Pradesh, India

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-99-5079-9 ISBN 978-981-99-5080-5 (eBook) https://doi.org/10.1007/978-981-99-5080-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Organizations

General Conference Chair Prof. (Dr.) Manas Kumar Mishra, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India

Program Chairs Dr. Goutam Sanyal, NIT Durgapur, India Dr. Frank Wang, University of Kent, England

Publication Chairs Dr. E. S. Pilli, Malaviya National Institute of Technology, Jaipur, India Dr. Valentina Emilia Balas, Aurel Vlaicu University of Arad, Romania

Conference Chair Dr. Madhu Sharma Gaur, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India

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Conference Convener Dr. Sanjeev Kumar, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India

Internal Advisory Committee Dr. Shashank Awasthi, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Dr. Satyendra Sharma, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Dr. R. K. Mishra, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Dr. P. C. Vashist, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Dr. Mohit Bansal, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Dr. Vinod Yadav, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Dr. Sansar Singh Chauhan, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Dr. Prashant Mukherjee, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Dr. Dinesh Kumar Singh, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India

Internal Steering Committee Dr. Rajiv Kumar, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Dr. Amrita Rai, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Dr. Upendra Dwivedi, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Dr. Rajeev Kumar, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India

Organizations

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Organizing Committee Dr. Raju Kumar, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Dr. Gaurav Jindal, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Dr. Kajal Rai, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Dr. Divya Misha, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Mr. Gaurav Bhaita, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Mr. Lalan Kumar, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Ms. Anju Mishra, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Ms. Deepkiran, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Ms. Jyoti Chauhan, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Mr. Virendra Kumar, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India Mr. Vikram Singh, GL Bajaj Institute of Technology and Management, Greater Noida, U.P. India

Preface

Technology is the driving force in this era of globalization for any country’s socioeconomic growth and sustained development. The influence of Cybersecurity and Data Engineering in shaping the process of global proliferation, particularly in technology, productivity, commercial, and financial spheres, is highly required. Data and security play a fundamental role in enabling people to rely on digital media for a better lifestyle. Now a day’s new applications and systems, such as social media, wearable devices, and drones, continue to emerge and generate even more data. With the COVID-19 pandemic, the need to stay online and exchange data has become even more crucial, as most of the fields, would they be industrial, educational, economic, or service-oriented, had to go online as best as they can. However, this digital transformation evolving with increased risks calls for highly robust and secure solutions with low resources and better performance that can adapt changes with constant mobility and flexibility. This growth in data exchange also comes with an increase in cyber-attacks and intrusion threats. Detecting cyber-attacks becomes a challenge, not only because of the sophistication of attacks but also because of the large scale and complex nature of today’s IT infrastructures. It is concluded that data science and security are a significant contributor to the success of the current initiative of Digital India. This book presents selected proceedings of the International Conference on Cybersecurity and Evolutionary Data Engineering (ICCEDE2022). The Conference ICCEDE2022 was held on 9th–11th December 2022, organized by the department of Master of Computer Applications (MCA), GL Bajaj Institute of Technology and Management, Greater Noida, Delhi NCR, India (affiliated to APJ Abdul Kalam Technical University, Lucknow, UP). The Aim of ICCEDE-2022 was to bring together vibrant smart and intelligent solution providers, stakeholders who share the passion for research and innovations including development partners, end-users and budding professionals across the globe to deliberate upon the different challenging aspects and issues in the field of Cybersecurity, Computational intelligence and evolutionary Data Engineering solutions.

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The Conference received 157 submissions and after critical quality only 31 papers were selected by the review committee for final presentation after rigorous blind reviews involving more than 240 reviewers. The committees of the conference include more than 300 National/International Committee chairs, Advisory board members, Technical program committee members, keynote speakers, presenters and experts from across the globe to share their views, innovations and accomplishments. We pay our heartfelt gratitude to Dr. Ram Kishore Agarwal, the chief patrons, and Shri Pankaj Agarwal and our Day 1 Keynote speakers to Dr. Raj Jain, Professor of Computer Science and Engineering, Washington University in St. Louis, Dr. Ljiljana Trajkovic, Ph.D., P.Eng., FIEEE, Simon Fraser University, Canada, Dr. V. K. Sharma, Dr. Arti Noor Sr. Director CDAC Noida for extending their cooperation for hosting students’ special session on Cyber Security Tools and Technologies and finally Dr. Prof.(Dr.) B. K. Verma for delivering day 2 keynote addresses. Our sincere thanks all the National/International Committee chairs, Advisory board members, Technical program committee members, keynote speakers, Session Chairs, research paper presenters, domain experts and participants from across the globe and the dedicated Organizing Committee members for their cooperation, hard work and support to make this conference successful. We also thank Springer for publishing the proceedings in the Lecture Notes in Electrical Engineering (LNEE) series. Special thanks to all the authors and participants for their contributions making an effective, successful and productive Conference. Washington, USA Las Palmas, Spain Greater Noida, India January 2023

Dr. Raj Jain Dr. Carlos M. Travieso Dr. Sanjeev Kumar

Keynotes

Title: Common Issues and Challenges in AI for Security Speaker

Dr. Raj Jain Barbara J. and Jerome R. Cox, Jr. Professor of Computer Science and Engineering, Washington University in Saint Louis Abstract AI is everywhere. It is being applied to security as well. In our research on the security of medical and industrial IoT over the last 5 years, we have noticed several common mistakes, challenges, and issues in applying AI and securing IoT. In this talk, we will discuss nine such common issues and mistakes. Biography Raj Jain is currently the Barbara J. and Jerome R. Cox, Jr., Professor of Computer Science and Engineering at Washington University in St. Louis. Dr. Jain is a Life xi

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Fellow of IEEE, a Fellow of ACM, a Fellow of AAAS, and a recipient of the 2017 ACM SIGCOMM Life-Time Achievement Award. Previously, he was one of the Cofounders of Nayna Networks, Inc., a Senior Consulting Engineer at Digital Equipment Corporation in Littleton, Mass, and then a professor of Computer and Information Sciences at Ohio State University in Columbus, Ohio. With 38,000+ citations, according to Google Scholar, he is one of the highly cited authors in computer science. Further information is at http://www.cse.wustl.edu/~jain/.

Title: Machine Learning for Detecting Internet Traffic Anomalies Speaker

Dr. Ljiljana Trajkovic Simon Fraser University, Canada Abstract Border Gateway Protocol (BGP) enables the Internet data routing. BGP anomalies may affect the Internet connectivity and cause routing disconnections, route flaps, and oscillations. Hence, detection of anomalous BGP routing dynamics is a topic of great interest in cybersecurity. Various anomaly and intrusion detection approaches based on machine learning have been employed to analyse BGP update messages collected from RIPE and Route Views collection sites. Survey of supervised and semisupervised machine learning algorithms for detecting BGP anomalies and intrusions is presented. Deep learning, broad learning, and gradient boosting decision tree algorithms are evaluated by creating models using collected datasets that contain Internet worms, power outages, and ransomware events.

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Biography Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, and the Ph.D. degree in electrical engineering from University of California at Los Angeles. She is currently a professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada. Her research interests include communication networks and dynamical systems. She served as IEEE Division X Delegate/Director and President of the IEEE Systems, Man, and Cybernetics Society and the IEEE Circuits and Systems Society. Dr. Trajkovic serves as Editor-in-Chief of the IEEE Transactions on Human-Machine Systems and Associate Editor-in-Chief of the IEEE Open Journal of Systems Engineering. She is a Distinguished Lecturer of the IEEE Circuits and System Society, a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society, and a Fellow of the IEEE.

Contents

Cybersecurity and Digital Forensic Current Status of Challenges in Data Security: A Review . . . . . . . . . . . . . . Neetika Prashar, Susheela Hooda, and Raju Kumar

3

Cyber Bullying: The Growing Menace in Cyber Space with Its Challenges and Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meenakshi Punia, Arjun Choudhary, and Ashish Tripathi

15

Hybrid Feature Extraction for Analysis of Network System Security—IDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. P. Anish, C. Shanmuganathan, D. Dhinakaran, and V. Vinoth Kumar

25

Stranger Trust Architecture: An Advancement to Zero Trust Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arjun Choudhary, Arun Chahar, Aditi Sharma, and Ashish Tripathi

37

Genetic Algorithm Optimized SVM for DoS Attack Detection in VANETs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ila Naqvi, Alka Chaudhary, and Anil Kumar

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Digital and IoT Forensic: Recent Trends, Methods and Challenges . . . . . Neha, Pooja Gupta, Ihtiram Raza Khan, and Mehtab Alam

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Cloud-Based Occlusion Aware Intrusion Detection System . . . . . . . . . . . . Deepak Sharma, Dipanshu Tiwari, Vinayak Singh, Priyank Pandey, and Vishan Kumar Gupta

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Comparative Analysis of Web Application Based Encryption Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuvraj Singh, Somendra Singh, Shreya Kandpal, Chandradeep Bhatt, Shiv Ashish dhondiyal, and Sunny Prakash

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Contents

Linking of Ontologies for Composition of Semantic Web Services Using Knowledge Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pooja Thapar and Lalit Sen Sharma

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Fake Image Dataset Generation of Sign Language Using GAN . . . . . . . . . 105 Anushka Kukreti, Ashish Garg, Ishika Goyal, and Divyanshu Bathla Analysis of Multimodal Biometric System Based on ECG Biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Sandeep Pratap Singh and Shamik Tiwari Performance Analysis of Nature Inspired Optimization Based Watermarking Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Vijay Krishna Pallaw and Kamred Udham Singh Evolutionary Data Engineering Applications A Review of Ensemble Methods Used in AI Applications . . . . . . . . . . . . . . 145 Priyanka Gupta, Abhay Pratap Singh, and Virendra Kumar Stress Detection Based on Multimodal Data in a Classroom Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 T. Swapna, A. Sharada, and M. Madhuri Comparative Study of Different Generations of Mobile Network . . . . . . . 171 Pooja Rani A Novel Framework for VM Selection and Placement in Cloud Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Krishan Tuli and Manisha Malhotra Quad Clustering Analysis and Energy Efficiency Evaluation in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Bhawnesh Kumar, Sanjiv Kumar, Harendra Singh Negi, and Ashwani Kumar Artificial Intelligence in Gaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Ritik Verma, Alka Chaudhary, Deepa Gupta, and Anil Kumar Analysis of Pulmonary Fibrosis Progression Using Machine Learning Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Shivani Agarwal, Avdhesh Gupta, Vishan Kumar Gupta, Akanksha Shukla, Anjali Sardana, and Priyank Pandey Lung Conditions Prognosis Using CNN Model . . . . . . . . . . . . . . . . . . . . . . . 225 Harshit Jain, Indrajeet Kumar, Isha N. Porwal, Khushi Jain, Komal Kunwar, Lalan Kumar, and Noor Mohd Stock Trend Prediction Using Candlestick Pattern . . . . . . . . . . . . . . . . . . . . 235 Divyanshu Bathla, Ashish Garg, and Sarika

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Motion Based Real-Time Siamese Multiple Object Tracker Model . . . . . 247 Vishal Kumar Kanaujia, Satya Prakash Yadav, Himanshu Mishra, Awadhesh Kumar, and Victor Hugo C. de Albuquerque Design and Development of IOT & AI Enabled Smart Entrance Monitoring Device . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Krishanu Kundu, Manas Singh, and Aditya Kumar Singh Sunlight-based Framework: An Approach for Energy Efficiency in IoT Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Priya Matta, Sanjeev Kukreti, and Sonal Malhotra Metaverse Technologies and Applications: A Study . . . . . . . . . . . . . . . . . . . 287 Sonali Vyas, Shaurya Gupta, and Mitali Chugh Body Sensor Networking: A Case Study of Heart Pulse Detection and Alert System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Kushagr Nandan, Aryan Tuteja, and Priya Matta Brand Sentiment Analytics Using Flume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Devanshi Sharma, Alka Chaudhary, and Anil Kumar The λNF Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Anurag Dutta and DeepKiran Munjal The Indian Search Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Anurag Dutta and Pijush Kanti Kumar COVID-19’s Influence on Buyers and Businesses . . . . . . . . . . . . . . . . . . . . . 343 John Harshith and Eswar Revanth Chigurupati Exploring Textural Behavior of Novel Coronavirus (SARS–CoV-2) Through UV Microscope Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 Amit Kumar Shakya, Ayushman Ramola, and Anurag Vidyarthi

Editors and Contributors

About the Editors Raj Jain is currently Barbara J. and Jerome R. Cox, Jr., Professor of Computer Science and Engineering at Washington University in St. Louis. Dr. Jain is a Life Fellow of IEEE, Fellow of ACM, Fellow of AAAS, and a recipient of the 2017 ACM SIGCOMM Life-Time Achievement Award. Previously, he was one of the cofounders of Nayna Networks, Inc., a Senior Consulting Engineer at Digital Equipment Corporation in Littleton, Mass, and then a Professor of Computer and Information Sciences at Ohio State University in Columbus, Ohio. He has 15 patents and has written or edited 12 books, 19 book chapters, 100+ journal and magazine papers, and 130+ conference papers. He is known for his research on the application of quantum computing, AI, and blockchain to cybersecurity, wireless technologies for IoT, 4G and unmanned aircraft systems, congestion control and avoidance, traffic modeling, performance analysis, and error analysis. His papers have been widely referenced. With 39,800+ citations, according to Google Scholar, he is one of the most highly cited authors in computer science. Carlos M. Travieso is a Professor and Head of the Signals and Communications Department at the University of Las Palmas de Gran Canaria, Spain. He received an M.Sc. degree in 1997 in Telecommunication Engineering at the Polytechnic University of Catalonia and a Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria, Spain. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has participated in 52 international and Spanish research projects, some of them as Lead Researcher. He is co-author of four books, Co-editor of 27 proceedings books, Guest Editor for eight JCR-ISI international journals, and Author of up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed in JCR—ISI—Web of Science) and has published seven patents in the Spanish Patent and Trademark Office.

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He is an Evaluator of the project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, in 2017, 2018, and 2019 editions and the Award in Modality of COVID Research in 2020. Sanjeev Kumar has 14+ years of teaching and 5-year research experience and currently working as an Assistant Professor in the Department of Master of Computer Applications at GL Bajaj Institute of Technology and Management, Greater Noida. He received M.C.A. from Utter Pradesh Technical University, Lucknow, and Ph.D. (Computer Science) from Gurukula Kangri University, Haridwar. He has qualified for UGC-NET in Computer Science and Application. He has guided 2 M.Tech. dissertations and 25 M.C.A. projects. He has published more than 20 research papers in International Journal and Conferences of Repute. He is the Editor of the book titled “Cyber Security in Intelligent Computing and Communications” published by Springer in the book Series of Studies in Computational Intelligence. His research interests are in Soft Computing, Computer Networks, Wireless Communication, 5G, and Security.

Contributors Shivani Agarwal Ajay Kumar Garg Engineering College, Ghaziabad, India Mehtab Alam Jamia Hamdard, New Delhi, India T. P. Anish Department of Computer Science and Engineering, R.M.K. College of Engineering and Technology, Chennai, India Divyanshu Bathla Computer Science, Graphic Era Hill University, Dehradun, India Chandradeep Bhatt Graphic Era Hill University, Dehradun, Uttarakhand, India Arun Chahar Center for Cyber Security, Security and Criminal Justice, Sardar Patel University of Police, Jodhpur, India Alka Chaudhary Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India Eswar Revanth Chigurupati Jawaharlal Nehru Technological University, Hyderabad, India Arjun Choudhary Center for Cyber Security, Sardar Patel University of Police, Security and Criminal Justice, Jodhpur, India Mitali Chugh UPES, Bidholi, Dehradun, Uttarakhand, India

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Victor Hugo C. de Albuquerque Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Fortaleza, CE, Brazil D. Dhinakaran Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India Shiv Ashish dhondiyal Graphic Era Deemed to be University, Dehradun, Uttarakhand, India Anurag Dutta Department of Computer Science and Engineering, Government College of Engineering and Textile Technology, Serampore, India Ashish Garg Computer Science, Graphic Era Deemed to Be University, Dehradun, India Ishika Goyal Computer Science, Graphic Era Deemed to Be University, Dehradun, India Avdhesh Gupta Ajay Kumar Garg Engineering College, Ghaziabad, India Deepa Gupta Amity Institute of Information Technology, Amity University Noida, Noida, Uttar Pradesh, India Pooja Gupta Jamia Hamdard, New Delhi, India Priyanka Gupta Suresh Gyan Vihar University, Jaipur, Rajasthan, India Shaurya Gupta UPES, Bidholi, Dehradun, Uttarakhand, India Vishan Kumar Gupta Graphic Era Deemed to be University, Dehradun, India John Harshith VIT University, Vellore, India Susheela Hooda Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India Harshit Jain Graphic Era Hill University, Dehradun, UK, India Khushi Jain Graphic Era Hill University, Dehradun, UK, India Vishal Kumar Kanaujia Computer Science and Engineering-Data Science Department, ABES Engineering College, Ghaziabad, Uttar Pradesh, India Shreya Kandpal Graphic Era Hill University, Dehradun, Uttarakhand, India Pijush Kanti Kumar Department of Information Technology, Government College of Engineering and Textile Technology, Serampore, India Ihtiram Raza Khan Jamia Hamdard, New Delhi, India Anushka Kukreti Computer Science, Graphic Era Deemed to Be University, Dehradun, India Sanjeev Kukreti CSE, Graphic Era Deemed to be University, Dehradun, India Anil Kumar School of Computing, DIT University, Dehradun, Uttarakhand, India

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Ashwani Kumar Shri Ram Group of Colleges, Muzaffarnagar, India Awadhesh Kumar Department of Computer Science and Engineering, Kamala Nehru In-Stitute of Technology Sultanpur—Kadipur Rd, Sultanpur, Uttar Pradesh, India Bhawnesh Kumar Graphic Era Deemed to be University, Dehradun, India Indrajeet Kumar Graphic Era Hill University, Dehradun, UK, India Lalan Kumar G. L. Bajaj Institute of Technology and Management, Greater Noida, UP, India Raju Kumar Department of Master of Computer Applications, G.L. Bajaj Institute of Technology and Management, Greater Noida, India Sanjiv Kumar Graphic Era Deemed to be University, Dehradun, India Virendra Kumar G L Bajaj Institute of Technology and Management, Greater Noida, India Krishanu Kundu Department of Electronics and Communication Engineering, G.L. Bajaj Institute of Technology and Management, Greater Noida, India Komal Kunwar Graphic Era Hill University, Dehradun, UK, India M. Madhuri G. Narayanamma Institute of Technology and Science, Hyderabad, India Manisha Malhotra University Institute of Computing, Chandigarh University, Mohali, India Sonal Malhotra Graphic Era Deemed to be University, Dehradun, India Priya Matta Computer Science & Engineering, Tula’s Institute, Dehradun, India Himanshu Mishra Department of CSE-APEX, Chandigarh University, Gharuan, Mohali, Punjab, India Noor Mohd Graphic Era University, Dehradun, UK, India DeepKiran Munjal Department of Computer Applications, G L Bajaj Institute of Technology and Management, Greater Noida, India Kushagr Nandan Graphic Era Deemed to be University, Dehradun, India Ila Naqvi AIIT, Amity University, Noida, Uttar Pradesh, India Harendra Singh Negi Graphic Era Deemed to be University, Dehradun, India Neha Jamia Hamdard, New Delhi, India Vijay Krishna Pallaw School of Computing, Graphic Era Hill University, Dehradun, India Priyank Pandey Graphic Era Deemed to be University, Dehradun, India

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Isha N. Porwal Graphic Era Hill University, Dehradun, UK, India Sunny Prakash GL Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, India Neetika Prashar Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India Abhay Pratap Singh G L Bajaj Institute of Technology and Management, Mathura, India Meenakshi Punia Department of Law, Sardar Patel University of Police, Security and Criminal Justice, Jodhpur, India Ayushman Ramola Department of Electronics and Communication Engineering, Sant Longowal Institute of Engineering and Technology, Sangrur, Punjab, India Pooja Rani G L Bajaj Institute of Technology and Management, Greater Noida, UP, India Anjali Sardana ABES Engineering College, Ghaziabad, India Sarika Computer Science Graphic Era Hill University, Dehradun, India Amit Kumar Shakya Department of Electronics and Communication Engineering, Sant Longowal Institute of Engineering and Technology, Sangrur, Punjab, India C. Shanmuganathan Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India A. Sharada G. Narayanamma Institute of Technology and Science, Hyderabad, India Aditi Sharma School of Engineering and Digital Sciences, Nazarbayev University, Asthana, Kazakhstan Deepak Sharma Bhai Parmanand DSEU, Shakarpur Campus-2, New Delhi, India Devanshi Sharma Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India Lalit Sen Sharma Department of Computer Science and IT, University of Jammu, Jammu, Jammu and Kashmir, India Akanksha Shukla Ajay Kumar Garg Engineering College, Ghaziabad, India Aditya Kumar Singh Research Intern Electrical and Electronics Department, IIT Guwahati, Guwahati, Assam, India Kamred Udham Singh School of Computing, Graphic Era Hill University, Dehradun, India

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Manas Singh Department of Electronics and Communication Engineering, G.L. Bajaj Institute of Technology and Management, Greater Noida, India Sandeep Pratap Singh School of Computer Science, UPES, Dehradun, India Somendra Singh Graphic Era Hill University, Dehradun, Uttarakhand, India Vinayak Singh Bhai Parmanand DSEU, Shakarpur Campus-2, New Delhi, India Yuvraj Singh Graphic Era Hill University, Dehradun, Uttarakhand, India T. Swapna G. Narayanamma Institute of Technology and Science, Hyderabad, India Pooja Thapar Department of Computer Science and IT, University of Jammu, Jammu, Jammu and Kashmir, India Dipanshu Tiwari Bhai Parmanand DSEU, Shakarpur Campus-2, New Delhi, India Shamik Tiwari School of Computer Science, UPES, Dehradun, India Ashish Tripathi Department of Information Technology, G. L. Bajaj Institute of Technology and Management, Greater Noida, India Krishan Tuli University Institute of Computing, Chandigarh University, Mohali, India Aryan Tuteja Graphic Era Deemed to be University, Dehradun, India Ritik Verma Amity Institute of Information Technology, Amity University Noida, Noida, Uttar Pradesh, India Anurag Vidyarthi Department of Electronics and Communication Engineering, Graphic Era (Deemed to Be University), Dehradun, Uttarakhand, India V. Vinoth Kumar Department of Information Technology, Velammal Institute of Technology, Chennai, India Sonali Vyas UPES, Bidholi, Dehradun, Uttarakhand, India Satya Prakash Yadav Department of Computer Science and Engineering, G.L. Bajaj Institute of Technology and Management (GLBITM), Greater Noida, India; Graduate Program in Telecommunications Engineering. (PPGET), Federal Institute of Education, Science, and Technology of Ceará (IFCE), Fortaleza-CE, Brazil

Cybersecurity and Digital Forensic

Current Status of Challenges in Data Security: A Review Neetika Prashar, Susheela Hooda, and Raju Kumar

Abstract Today, data and its security are utmost important for all organizations. The data security and privacy are the most common issues in all the sectors including personal as well as business. With the rise of digital services, data protection and security has become the most critical key areas to respect and safeguard privacy. Data that can be private or public but it needs a secure environment throughout. Organizations that provide digital services must have defined and key processes which include protection regarding the privacy of every individual. In this paper, various types of data security, the risks faced in data security, solutions granted for the problems using technologies, data masking and data mining techniques have been discussed. In addition, this paper also enlightens the recent challenges in data security which will be helpful for novice researchers. Keywords Data security · Challenges in data security · Data mining · Data masking · Big data · Importance of security and privacy

1 Introduction For all corporations including both public and private businesses, data security is the utmost preference, as the rising abuse to data privacy is so to preserve integrity, profits and records. Issues around data security, privacy and protections are today at brisk focus than ever before. Without data security the generation is at risk to be exploited for identity fraud, theft, destruction of property, and much worse [1]. The ultimate set of processes or digital practices of protecting one’s privacy or sensitivity from being exploited by any unauthorized or harsh access is defined as data N. Prashar · S. Hooda (B) Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India e-mail: [email protected] R. Kumar Department of Master of Computer Applications, G.L. Bajaj Institute of Technology and Management, Greater Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_1

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security or information security or computer security. The internal security mechanism of data security is the main concern of this paper. As, the internal security holds four parameters—access, flow, inference and other cryptographic. The parameters discuss work on the practical implementations as well as limitations which helps them in achieving all objectives and goals in every condition [2]. Data security is the crucial element to regulatory compliance, no matter what industries or foundations operate in. The three core elements related to data security are Confidentiality, Integrity, and Availability. This concept is also wellacknowledged as CIA triad which describes the functioning of the security model and framework that says data should be accessed to only authorized users, the data saved should be reliable, accurate and not allowed to unwanted changes, and the data present should be always accessible to the people needs everywhere. User privacy configuration tools are highly efficient and essential in order to provide the best data protection in terms of respecting and guarding the privacy of the client. More work is expected to be done in future on probabilistic Investigation and hybrid collaborative filtering systems in reference to provide the users a better option in cases of big and sparse data [3]. Data masking is the practice of creating a duplicate of your organizational data, which includes changing the data values using the same format. It is very useful in dissolving critical threats like data loss, account hacking or compromising, data exfiltration. For this, CDMF (Commercial Data Masking Facility) is also introduced in the market because of its rising demand. The very main function of CDMF is to implement data privacy and the systems can be exported to USA to any of the client or user throughout the world [4]. Data mining which is also termed as knowledge discovery in databases. Also, data mining faces various challenges like usefulness, uncertainty and expressiveness of data, handling different types of data, maintaining privacy and data security, scalability as well as efficiency in data mining, data mining at different abstraction levels. Researchers have contributed their time in data mining as it is one of the most expanding processes with new outcomes [5, 6]. In this paper, data security and its types, big data and its challenges, various data mining and data masking techniques have been discussed in detail. Major challenges in data security also have been addressed in this paper so that novice researchers can do work in future. The rest of the paper is organized as follows. Section 2 describes the basic concepts and background. Section 3 depicts the various data mining techniques and Section 4 describes various data masking techniques. Section 5 presents the challenges of data security and Section 6 is the conclusion of the work.

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2 Literature Survey Data security is the major concern for every organization. But, as reported from the literature, there is a deficit in pros and cons of existing data security techniques. Comparison of various data masking and mining techniques and challenges in data security have been presented in this paper. The authors in [5] describe the origin of data and models based on the need of organization or business which deals with further problems of people. Next, the term Big Data came into existence. Big data is divided into 3 categories as below [6]. ● Data is collected from different objects, during operations which includes digital devices. ● Different types of data that are retrieved from people, including the audios, videos and others that are used by people. ● Data which is generated with the help of machines. Most of the social platforms including Facebook, Telegram, Twitter, LinkedIn also allow unknown parties to enter into their data which harms their privacy and leads to data breach. Various types of data security have been discussed below: ● Access control—This security measure talks about the set of policies for restricting unauthorized access including unwanted users, or not allowed organizations. It decides who has the authority to use the credentials and further access the data. Access control including many can also accompany ownership, that is beneficial in the systems having long-term storage of one own set of data. The author describes the parking of 3 assumptions—unanticipated user, proper user identification, and privilege—information is protected [7]. ● Authentication—The practice of allowing authorized users to access the information or data. Therefore, it is the practice to find out the data is genuine. Every object in IoT also feels the need for authentication in order to respect the official user access, so to ensure the identity between several objects as described by the authors in [7]. Various research studies to authenticate information exchange schemes in WSN and sensor have also been worked on by the authors [8]. ● Backups and Recovery—The process of backing up data in case of loss and managing the system in such a way that it allows data recovery due to data loss. This way it protects your data from getting lost and keeps it safe. Data backup is known to be backing up business data of business data, information security integrated management systems, and managed systems as prescribed by the authors [9]. ● Data masking—The technique of storing the carbon copy of data so that the structure of information remains the same, but the information is changed. Or we can say that it is the practice to hide the real and genuine data by practicing several ways. ● Data resiliency—Building the data resiliency into the hardware as well as software states that the data is safe even in case of natural disasters or power outages. It is defined as saving the data from any destructive causes.

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Fig. 1 Layers of data security

● Encryption—The practice of converting the desired text to an unreadable format in order to save the data from theft, breach, and others. It is further parted into two categories i.e. symmetric (single key which both uses cipher and decipher to unfold the data) and asymmetric encryption (different keys are used here for the coding and then encoding). Also, in Zuo et al. [10] the authors describe that the blockchain-based ciphertext-policy attribute-based encryption scheme i.e., BCAS stores the hash of the key, hash of the decrypted data in blockchain, hash of ciphertext. Figure 1 displays the various layers of security around the information, hence it uses the CIA Triad i.e., Confidentiality, Integrity, and Availability (authentication and authorization) to prevent any type of data harm. The challenges to big data are a leading topic as it is difficult to process, maintain and store the integrity of data. Few Challenges are listed as efficiently storing and searching the data, effective ways to analyse the data, machine learning techniques for big data mining, and efficiently handling the big data [11]. Most efficient big data security solutions include: ● Data encryption—Encryption tools are used to secure a massive volume of different data types. The data is converted to unreadable format or user-generated code. Very popular DES known as Data Encryption Standard is found to be the most powerful and strong in terms of vulnerable attacks. The true random generators to produce unpredictable sequences of bits are used in advanced encryption [11]. ● User access Control—This works as the most efficient tool to manage the security issues by the policy-based approach to automate the access data. There are various authentication factors like Password/ PIN, Bio-metric measurement and using card or key. Further access control is categorized into two categories which are Physical Access Control (includes organizations, rooms, campuses) and Logical Access Control (includes computer networks, system files and data). The introduction of

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PASH i.e., privacy-aware s-health access control which describes the key ingredient as a large universe CP-ABE with access policies, where only attributes are revealed [12]. ● Attack detection and prevention—This method applies the IPS known as Intrusion Prevention System in order to create protection from examining network traffic. IDS isolates the intrusion before it significantly damages the database and the system. A recent survey of cybersecurity that included data mining as well as machine-learning algorithms judges the different methods like genetic algorithm, clustering, support vector machine, decision tree, naive Bayes, and random forest [13]. ● Protect non-relational data, data storage, and transaction logs—The non-relational databases are vulnerable, and can be protected by AES, known as Advanced Encryption System. ● Protect distributed programming framework—Programming framework can be described as the bundle of tools used by programmers to create something unique or bring up something new.

3 Data Mining Techniques Smoothening, data integration, data transformation, generalization, aggregation, normalization, and data reduction are few ways which come under the data mining which shields and concaves our data from outside malpractices for savage privacy and data. Data mining techniques [14] are constantly increasing which are used in identifying attacks, anomalies or intrusion in protected network environments. It is the process of discovering interesting patterns and knowledge from big data [15]. ● Association—It is known to be one of the best ways of data mining techniques. It is the process of extracting association rules in order to get the attribute-value conditions that take place frequently together in each set of data. ● Classification—It is one of the classic data mining techniques which is based on machine learning. The method of searching models or you can say functions that explain and differentiate concepts/ data classes to aim to make use of the model for prediction of class of the object, whose Classifiers include Decision classifier, K-MN classifier, Rule-based classification, Fuzzy logic, rough set theory etc. [16] ● Prediction—The combinations of data mining techniques like trends, classifications and clustering etc. ● Regression—The data mining process which identifies and analyses the relationship between variables because of the presence of some more factors. ● Sequential patterns—The data mining technique that is known for evaluating and examining sequential data to create the sequential patterns. ● Clustering—It is defined as separation of information/data into parts/groups of connected objects. It acknowledges and accomplishes improvement. ● Outer detection—This technique is used for the observation of data items in a set of data, which is unique.

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Fig. 2 Data mining techniques

● Decision Tree—It is found to be commonly used techniques among all other techniques of data mining because the model used in this is very easy to remember and grasp. ● Artificial Neural Network (ANN) Classifier Method—It is also called as “Neural Network”. ANN helps in building/moulding the structure-supported information which flows during the learning part [17, 18]. The Fig. 2 describes the various data mining techniques like classification, regression, sequential patterns, outer, in order to safeguard our data from any harm.

4 Data Masking Techniques Data Masking also has an application key with respect to Open Government Data (OGD). The brief summary on data masking techniques have also been discussed in preliminary and standard uses in the concept of OGD. In OGD, data masking comes out to be the finest approach to protect data privacy [19]. ● Encryption—The most multiplexed and secure way of data masking which comprises encryption algorithms which further require the key to decrypt/decode the text. ● Scrambling—The way which uses the jumbling of characters and numbers in some random order, therefore hides the original data. ● Nulling out/deletion—This technique covers the data by applying some value which is null to the data column, to make sure of the avoidance of any unauthorized access. It is useful where the data is not sufficient, and is not applicable in an environment of testing. ● Substitution—The way of masking in which the data is substituted with another value. This is the most effective method to safeguard the original data. The advantage of this method is that the data comes out to be more realistic, but the disadvantage is that it is not suited for big data or large amounts of information.

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Fig. 3 Process of data masking

● Shuffling—It is quite like substitution, but at the same time, this method makes use of a data column for shuffling the data in any random manner. This technique is useful in big data and also keeps it realistic. But on the other hand, if the data is too short or small, it can be undone very easily. ● Data aging—This way either decreases or increases the data field based on the data masking policy with consideration of data range. It is mostly appropriate for numeric data. ● Number and date variance—This is only applicable for masking the most important financial and transaction of data information. Few of the applications of data masking are auditing, cryptography and access control. Figure 3 discusses the process of data masking. It explains the whole process that is carried out to mask our data.

5 Challenges in Data Security Recent trends and challenges have been discussed include big data like data acquisition, data collation, data extraction, data visualization, data structuring and data interpretation. So, it becomes an urgent need that can efficiently hold our data into safe hands without any discrepancy. ● Store and management process challenges—There is need of dramatic increase in safer distributed schemes with trusted robustness and storage [20]. ● Fake data—It is an important security concern about big data because it reduces your ability to identify other issues. ● Problems with data mining solutions—The data mining solutions has a falsepositive rate towards data security, as the algorithms used turns to be very complex and moreover the data needs to be presented in integrated form, so it creates the

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problem. One of the biggest problems we found here is that data is stored over one single server. Issues with data masking features—It is a massive risk to all sensitive information because it ensures the separation of confidential information on clients from the actual data. It is one of the effective ways in securing data at risk. This feature can secure data vulnerable situations and is cost-effective. Difficult to protect complex big data—The more complex data sets are, the more difficult it becomes to protect it. Using strong encryption techniques for backing up data to avoid data leakages [21]. Loss of data access control—Access control in data security is very crucial to ensure that information does get disclosed to unauthorized users or organizations. Less budget for data security—The problem with information security is that most foundations are forced to balance the potential risk against their available budget. The lack of appropriate budget is a common theme. Data poisoning—Data poisoning has the potential to bring about catastrophic damage to national security in the age of artificial intelligence. It is the way by which the bad data is injected into the original data set. Secure data storage and transaction logs, real-time security monitoring, cryptographically enforced data-centric security, scalable and composable privacypreserving data mining and analytics [22].

6 Importance of Security and Privacy We find that technology is worth in number of ways as it has provided us more communication with people around the world, access to enhance our knowledge and much more [23, 24]. Overall, technology acts as a boon for us as it had made our life easier, and things a lot less private which makes us more attentive to draw our attention to be extra careful with security on Internet. Internet security is very important to protect our privacy, protect us from fraud and from different types of viruses that could destroy our privacy as well as technology. Security issues with wireless technology has been continuously rising [25]. Although educational or awareness issues (from simply information security guidelines to well-developed information security education programmes) are security matters in nearly all organizations in the era of the information society, their nature is not well understood resulting, for example, in the ineffectiveness of security guidelines or programs in practice. In this regard it will be shown that even passing around security guidelines in a factual manner per se, for instance (i.e., their presentation as normal facts, at the phrastic level), as is likely to be the case in most organizations, may be an inapt approach as such [26, 27]. Despite large investments in information security, security incidents continue to grow and organizations are allocating more resources toward advanced security systems, often requiring compliance behaviour to minimize information security threats and incidents. Thus, when a behaviour is performed, it is likely that prior observations were relevant, memory sufficiently represented the stimulus and

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knowledge was retained. In other words, the individual has the necessary awareness to motivate behaviour [28]. Online privacy and online security are a critical factor in preventing fraud both personally and in business too [29, 30]. Data that is extremely complex including the problem of sorting, indexing, visualizing, searching and analysing turns out to be the major challenges for today’s different organizations [31]. Big data requires certain scalable architectures than can be used for storing and processing data, So, Cloud computing represents a cost-effective and practical solution for Big Data storage, processing and moreover for sophisticated various analytics applications [32]. The key characteristics of Manufacturing Internet of Things, are the life cycles of big data analytics for MIoT, the necessities of big data analytics for MIoT [33] have also been introduced by the author to draw an outline in aspects of security and privacy. Recent developments in sensor networks, cyber-physical systems, and the ubiquity of the Internet of Things (IoT) have increased the collection of data (including health care, social media, smart cities, agriculture, finance, education, and more) to an enormous scale. However, the data collected from sensors, social media, financial records, etc. is inherently uncertain due to noise, incompleteness, and inconsistency. The analysis of such massive amounts of data requires advanced analytical techniques for efficiently reviewing and/ or predicting future courses of action with high precision and advanced decision-making strategies [34–37].

7 Conclusion Data protection is a rising concern evidenced with an upgrade in the count of reported incidents for the loss of not authorized access which leads to exposure of sensitive and private data/information. The need to understand the security of data is important as the bulk of data collected, retained and shared digitally is increasing day by day. As the world, is growing out to be digital in coming future, everything that is needed like from personal information about an individual to data about any authority, organization, institution or business department, every information can be founded over internet which is a very good way to modernize the world, but simultaneously it has been observed data insecurity breaches are on the increasing note. Various types of data security, the risks faced in data security, solutions granted for the problems using technologies, data masking and data mining techniques have been discussed in this paper. In addition, this paper also enlightens the recent challenges in data security which will be helpful for those researchers who want to pursue research in this area.

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References 1. Denning DE, Denning PJ (1979) Data security. ACM Comput Surv (CSUR) 11(3):227–249 2. Deepika S, Pandiaraja P (2013) Ensuring CIA triad for user data using collaborative filtering mechanism. In: International conference on information communication and embedded systems (ICICES). IEEE 3. Johnson DB, Matyas SM, Le AV, Wilkins JD (1993) Design of the commercial data masking facility data privacy algorithm. In: Proceedings of the 1st ACM conference on Computer and communications security, pp 93–96 4. Chen MS, Han J, Yu PS (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8(6):866–883 5. Zhang D (2018) Big data security and privacy protection. In: 8th international conference on management and computer science (ICMCS 2018), vol 77, pp 275–278. Atlantis Press 6. Denning DE, Denning PJ (1979) Data security. ACM Comput Surv (CSUR) 11(3):227–249 7. Bhardwaj I, Kumar A, Bansal M (2017) A review on lightweight cryptography algorithms for data security and authentication in IoTs. In: 2017 4th international conference on signal processing, computing and control (ISPCC). IEEE, pp 504–509 8. Yang SK, Shiue YM, Su ZY, Liu IH, Liu CG (2020) An authentication information exchange scheme. In: WSN for IoT applications, vol 8. IEEE Access, pp 9728–9738 9. Zhao Y, Lu N (2018) Research and implementation of data storage backup. In: 2018 IEEE international conference on energy Internet (ICEI), pp 181–184 10. Zuo Y, Kang Z, Xu J, Chen Z (2021) BCAS: a blockchain-based cipher text-policy attributebased encryption scheme for cloud data security sharing. Int J Distrib Sens Netw 17(3) 11. Yazdeen AA, Zeebaree SR, Sadeeq MM, Kak SF, Ahmed OM, Zebari RR (2021) FPGA implementations for data encryption and decryption via concurrent and parallel computation: a review. Qubahan Acad J 1(2):8–16 12. Zhang Y, Zheng D, Deng RH (2018) Security and privacy in smart health: efficient policy-hiding attribute-based access control. IEEE Internet Things J 5(3):2130–2145 13. Zhang F, Kodituwakku HADE, Hines JW, Coble J (2019) Multilayer data-driven cyber-attack detection system for industrial control systems based on network, system, and process data. IEEE Trans Industr Inf 15(7):4362–4369 14. Salo F, Injadat M, Nassif AB, Shami A, Essex A (2018) Data mining techniques in intrusion detection systems: a systematic literature review. In: IEEE Access, vol 6, pp 56046–56058 15. Xu L, Jiang C, Wang J, Yuan J, Ren Y (2014) Information security in big data: privacy and data mining. In: IEEE Access, vol 2, pp 1149–1176 16. Siraj MM, Rahmat NA, Din MM (2019) A survey on privacy preserving data mining approaches and techniques. In: Proceedings of the 2019 8th international conference on software and computer applications, pp 65–69 17. Zhou H, Sun G, Fu S, Liu J, Zhou X, Zhou J (2019) A big data mining approach of PSObased BP neural network for financial risk management with IoT. In: IEEE Access, vol 7, pp 154035–154043 18. Salo F, Injadat M, Nassif AB, Shami A, Essex A (2018) Data mining techniques in intrusion detection systems: a systematic literature review. In: IEEE Access, vol 6, pp 56046–56058 19. Basin D (2021) The cyber security body of knowledge: formal methods for security, version 20. Bao R, Chen Z, Obaidat MS (2018) Challenges and techniques in big data security and privacy: a review. Secur Privacy 1(4):e13 21. Kumar PR, Raj PH, Jelciana P (2018) Exploring data security issues and solutions in cloud computing. Procedia Comput Sci 125:691–697 22. Tarekegn GB, Munaye YY (2016) Big data: security issues, challenges and future scope. J Comput Eng Technol 7(4):12–24 23. Denning DE, Denning PJ (1979) Data security. ACM Comput Surv (CSUR) 11(3):227–249 24. Fang W, Wen XZ, Zheng Y, Zhou M (2017) A survey of big data security and privacy preserving. IETE Tech Rev 34(5):544–560

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25. Daeef A, Mohamed M, Basir N, Saudi MM (2017) Security awareness training: a review. In: Lecture notes in engineering and computer science 26. Singh P, Kumar S, Kumar A, Usama M (2022) Machine learning based intrusion detection system for minority attacks classification. In: 2022 international conference on computational intelligence and sustainable engineering solutions (CISES), pp 256–261. https://doi.org/10. 1109/CISES54857.2022.9844381 27. Jaeger L (2018) Information security awareness: literature review and integrative framework 28. Hwang I, Wakefield R, Kim S, Kim T (2021) Security awareness: the first step in information security compliance behavior. J Comput Inform Syst 61(4):345–356 29. Cricchio L, Grazia M, Palladino BE, Eleftheriou A, Nocentini A, Menesini E. Parental mediation strategies and their role on youths’ online privacy disclosure and protection: a systematic review. Eur Psychol 27(2):116 30. Durnell E, Karynna OM, Howell RT, Zizi M (2020) Online privacy breaches, offline consequences: construction and validation of the concerns with the protection of informational privacy scale. Int J Hum–Comput Interact 36(19):1834–1848 31. Naeem M, Jamal T, Jorge DM, Butt SA, Montesano N, Tariq MI, De-la-Hoz-Franco E, DeLa-Hoz-Valdiris E (2022) Trends and future perspective challenges in big data. In: Advances in intelligent data analysis and applications. Springer, Singapore, pp 309–325 32. Elshawi R, Sakr S, Talia D, Trunfio P (2018) Big data systems meet machine learning challenges: towards big data science as a service. Big Data Res 14:1–11 33. Dai H-N, Wang H, Xu G, Wan J, Imran M (2020) Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies. Enterp Inform Syst 14(9–10):1279–1303 34. Hooda S, Lamba V, Kaur A (2021) AI and soft computing techniques for securing cloud and edge computing: a systematic review. In: International conference on information systems and computer networks (ISCON), pp 1–5 35. Datta P, Sharma B (2017) A survey on IoT architectures, protocols, security and smart city based applications. In: 8th international conference on computing, communications and networking technologies, ICCCNT 36. Sangeeta TU. Factors influencing adoption of online teaching by school teachers: a study during COVID-19 pandemic. J Pub Aff 21(4):e2503 37. Kaur P, Harnal S, Tiwari R, Upadhyay S, Bhatia S, Mashat A, Alabdali AM (2022) Recognition of leaf disease using hybrid convolutional neural network by applying feature reduction. Sensors (Basel) 22(2):575. https://doi.org/10.3390/s22020575. PMID: 35062534; PMCID: PMC8779777

Cyber Bullying: The Growing Menace in Cyber Space with Its Challenges and Solutions Meenakshi Punia , Arjun Choudhary , and Ashish Tripathi

Abstract Cyberbullying is upgraded and digitized form of criminal intimidation with various forms and types even in cyber world. The menace of cyberbullying has metamorphosed not just into a technology-based crime but has its magnificent impact on one’s digital as well as physical life. To have insight into various legal and technological aspects of it, research has been conducted through collecting primary data from an online and physical survey / questionnaire. Role of AI, limitations of law and technology in response to control and check cyberbullying have been scrutinized in this paper. Keywords Cybercrime · Cyber bullying · IT Act 2000 · Cyber crime portal · Nirbhaya

1 Introduction The Internet offers children and young people many opportunities for growth, including advantages like social support, identity exploration, and the development of interpersonal and critical thinking abilities, as well as the educational advantages of vast knowledge availability, academic support, and global cross-cultural interactions. The past ten years have dramatically changed how people engage with one another, approach learning, and consume entertainment. Technology has most significantly produced new communication instruments. The devices are very popular with M. Punia Department of Law, Sardar Patel University of Police, Security and Criminal Justice, Jodhpur, India e-mail: [email protected] A. Choudhary (B) Center for Cyber Security, Sardar Patel University of Police, Security and Criminal Justice, Jodhpur, India A. Tripathi Department of Information Technology, G. L. Bajaj Institute of Technology and Management, Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_2

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young individuals, who frequently use the internet, instant Emails, chat rooms, social networking sites, webcams, and text messaging.

1.1 Definitions of Bullying A person is bullied when he is repeatedly subjected to negative actions on the part of one or more other people. According to the American Psychological Association’s 2018 definition [1]. Bullying is “a violent behavior where someone intentionally and persistently causes another person’s pain or suffering.” Bullying can occur through physical interactions, phrases, or more subdued acts. Usually, it is difficult for the bully to stand up to him. Bullying himself or herself and taking no action to “cause” the bully. According to the Bar Association of India [2], bullying is systematically and repeatedly causing physical harm or mental pain to another person, one or more employees, or students. It has also been described as unwanted and repetitive. Written, oral.

1.2 Types of Bullying Physical and verbal abuse are among the direct types of bullying. It consists of an outward display of force. Further research showed that higher participation in all forms was associated with bullying categories of parental support. More bullying was correlated with more friends Aad less abuse in verbal, physical, and interpersonal forms but was not related to online bullying (Fig. 1).

2 Bullying on the Internet The Internet offers children and young people many opportunities for growth, including advantages like social support, identity exploration, and the development of interpersonal and critical thinking abilities, as well as the educational advantages of vast knowledge availability, academic support, and global cross-cultural interactions. The past ten years have dramatically changed how people engage with one another, approach learning, and consume entertainment [3]. Technology has most significantly produced new communication instruments. The devices are very popular with young individuals, who frequently use the internet, instant Emails, chat rooms, social networking sites, webcams, and text messaging. Bullying causes memories to be created that frequently endure a lifetime. Bullying can occasionally lead to extreme, cruel behavior since it can damage a person’s mental and physical health [4]. Ironically, harassment frequently begins at the school level, where it is most prevalent. Generally, widespread the prevalence of suicides and

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Fig. 1 Types of bullying

PHYSICAL

VERBAL

CYBER TYPES OF BULLYING

SOCIAL

PREJUDICIAL

RELATIONAL

homicides linked to harassment is widely documented, creating urgency to avert tragic outcomes and save casualties. One type of Online misconduct that has hurt the society is referred to as Online bullying Traditional harassment that used to be a dominating display and a combining economic success with the effective use of physical force and fear and trepidation for the weaker and more defenseless with the development of internet technology, bullying has also increased in cyber communities, but in a different form [5]. Cyberbullying may be defined as a malicious act intended to cause harm that is carried out using technology. Unlike traditional harassment, which was confined to public spaces and educational institutions. Backyards and countless cutting-edge daily devices have also introduced cyberbullying into people’s homes. Digital harassment occurs through various channels, including emails, internet chat, text messages, entertainment, and social networks [6]. These social media posts from many platforms demonstrate the phenomenon of cyberbullying that goes the place. ● You are overweight and ugly. You do not have any friends, and you will never find love. ● How does it feel to be the target of everyone’s hatred right now? You are a shame to the human species and a puke. ● You are so in need…STOOPID SLAG! Objective of this research are mentioned below: a. To examine cyberbullying, create a complete social network dataset. b. It is to look at cyberbullying in Rajasthani schools among students too.

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3 Literature Review over Cyber Bullying Cyberstalking is a severe issue that will only get worse as more people use the Internet and other telecommunications technologies, according to current trends and research. The book by Paul Bocij provides a foundation for the first solution to the issue. He provides the reader with a perceptive look at how communications technology provides a comprehensive examination of an understudied subject. Anyone worried about the crime of cyberstalking must read it. Cyberstalking, a brand-new type of aberrant behavior, involves using technology to harass people in various ways [7]. Cyberstalking has become an increasingly prevalent social issue that can harm computer users everywhere in the world in less than ten years as a result of our reliance on the Internet, email, instant messaging, chat rooms, and other communications technology [8]. Bullying, both conventional and online, causes stress in individuals who are bullied and witness bullying, absenteeism, and staff turnover [9]. They damage businesses’ reputations and make it challenging for them to find qualified employees. Both conventional bullying and cyberbullying result in lawsuits and increased expenses for organizations [10]. Cyberbullying has the potential to cause more harm than conventional bullying. The perpetrator of the bullying as well as any witnesses may be anonymous, which makes it possible for them to extend the victims’ suffering. It is time to end bullying, whether it takes the form of physical bullying or cyberbullying, to save people’s lives, health, and safety [11]. Millions of people lose out on their personal and professional achievements as a result of cyberbullying and cyberstalking. The majority of people are aware of how serious a problem this is, yet they are powerless to alter their circumstances since they have been thinking in this way for such a long time. The fact is that if you or your child is experiencing limits as a result of cyberbullying and hasn’t been able to change, it’s likely because you don’t have an effective approach to dealing with these trying circumstances. This research explores the reasons for cyberbullying, how to protect someone from a stalker or bully, what to do if your child is engaging in cyberbullying, and a step-by-step plan that helps.

4 Governmental Efforts and Legislation Provision in Indian Perspective Cyberbullies can be stopped using the various laws stated under the provisions of the Indian Penal Code (IPC) 1860 and the Information Technology Act, 2000 (IT Act). An indigenous reporting platform for complaints about cybercrime has been created, and several further government efforts are being considered [12]. Legal remedies for the providing relief under various Acts are mentioned below: ● Indian Contract Act, 1872 ● Indian Penal Code, 1860

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● Information Technology Act, 2000 (as amended in 2008) ● Information Technology (Intermediately Guideline) Rules, 2011 ● The Juvenile Justice (Care and Protection of Children) Act, 2000 (as amended in 2006 and 2010) ● The Constitution of India, 1950 ● The Commissions for Protection of Child Rights Act, 2005 ● The Criminal Procedure Code, 1973 ● The Protection of Children from Sexual Offences Act, 2012 ● The Young Persons (Harmful Publications) Act, 1956 Other than legislative provisions regarding curbing of cyber bullying there are other government supported programs in this direction.

4.1 The Nirbhaya Funds Scheme It is a project of the Indian government under the Nirbhaya funding program for safeguarding women’s and children’s safety. A response from the ministry of home affairs is a single digit. It was categorized as part of the Emergency reaction support system (ERSS) to handle emergencies requiring rapid aid from law enforcement, fire, rescue, or any other help.

4.2 Cybercrime Prevention Against Women and Children Scheme (CCPWC Scheme) A grant of INR 87.12 crore was given to states and UTs under the CCPWC scheme for establishing cyber forensic training labs and preventing cybercrime. In addition, INR 6 crores was granted to improve training programs for police and prosecutors. Beneath Different entities are established under the CCPWC program and are in charge of reporting online. Investigating criminal activity, studying complaints on cybercrime, and looking for any scary state of cybercrime.

4.3 Indian Cybercrime Coordination Center (I4C) Scheme I4C serves as a crucial instrument in the fight against cybercrime by preventing unauthorized usage of social space. Additionally, it is aided in working on many projects by multinational organizations and the rapid growth of technology. Its goal is to address various concerns raised by internet media, focusing on women and increasing young awareness of child victimization.

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4.4 The National Cyber Crime Reporting Portal (NCCR Portal) NCCR allows for the submission of online complaints.by the criticized victims, particularly women and children. With the aid of the local police, this portal offers prompt action on the reports of the criminal activity. Because technology has surpassed every established method, it has also proved to be extremely helpful in dissemination of the cyber-crime cases [13]. On this portal one can file complaints about facilitating the nationwide cybercrime reporting process in the National Cyber Crime Reporting Portal. It also makes it possible for victims and complainants to get in touch with cybercrime cells and access all relevant information at their fingertips. Victim has to register on the website https://cybercrime.gov.in. Further action will be forwarded by the state response team to the nearest police station to the victim location.

5 Findings of the Survey In order to understand the current situation in better manner, a questionnaire was prepared and a survey was done among various personnel. Total respondents were around 594 and were from different parts of India with varying academic qualifications along with age group. Male and Female ratio of respondents was 53:47 respectively. Out of all 39.4% of respondents to the survey believed that the age group between 18 and 25 engages in acts related with cyberbullying. 35.6% are from 25 to 35 age group and surprisingly 21.6% were from the age group of 13–18 and the rest was 3.4% from 45 and above. The major findings of this survey are that 32.3% of respondents think that women are bullied more than men, 10.5% think that men are bullied more, 47.7% think that both sexes are equally bullied, and 9.5% would rather not comment. However, the survey’s actual findings support the respondents’ perceptions. The majority, 54% of males, report being bullied, and approximately Bullying affects 51% of females. In actuality, there is little difference in their percentages of bullying. In response to the question about “what are the various types/forms of cyberbullying faced by people?”. The majority perception is body shaming, which is clubbed with mean comments on the online platforms, disability and fashion sense. This whole came with 54% agreeing to body shaming as part of cyber bullying. 24% of total respondents agreed that mean statements on economic background and ego were used to bully someone online. Similarly, on sexuality 20% respondents spurt out as third major form of bullying online. Interestingly surprising trolling, random obscene message and fake image came under 6% as cyberbullying, which giving away the perception that the bullying is being done.

Cyber Bullying: The Growing Menace in Cyber Space with Its … Table 1 Cyber bullying types and the responses in survey done

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S. no

Cyberbullying type

Response

1

Body shaming

39 (42.4%)

2

Disability

23 (25%)

3

Commenting

50 (54.3%)

4

Fashion sense

32 (34.8%)

5

Senior/junior

27 (29.3%)

6

economically/financially

25 (27.2%)

7

Sexuality

22 (23.9%)

8

Family background

22 (23.9%)

9

Ego

43 (46.7%)

10

No

7 (7.6%)

11

Some random obscene messages

1 (1.1%)

12

Abuse language and fake editing

1 (1.1%)

13

Trolling

1 (1.1%)

Over the question of “which social media platform, people have experienced cyberbullying often?”. In which more than 65% respondents mentioned about Facebook, WhatsApp, Instagram (Meta Inc.) second to these were Telegram, Snapchat Twitter, with 29% of respondents, third with 10% comprising of dating apps like Tinder, Bumble etc. and under 2% were apps like Omegle, Twitch, YouTube. which is surprising with the obvious perception. Online Cyberbullying is mistreatment when someone bullies another personnel on the internet. Among the responses 66.3% of the respondents agreed upon, 49% respondents to the survey accepted that cyber bullying is done by sending mean messages to the victims, 45% respondents were of opinion that bullying is done by calling and 43% accepted that using someone’s else cell phone to trouble others or 42% of the respondents agreed upon that many of online users pretend to be an another person online. Out of all 594, 54% accepted that cyber bullying is done by sending threatening/hateful messages to someone and share a deformed/morphed/ deep fake of victims on social media platforms, which is being confirmed with NCRB 2021 data. Since spreading misinformation/rumor/hoax about someone is the perfect cyberbullying someone. Hence, regarding this when respondents were asked whether they shared rumors about someone via cyberspace. 48.6% responded to sharing information of someone 42% shared and forwarded unverified information about someone they know. The Quad of knowns being friends, relatives, colleagues and acquaintances. It seems things are going quid pro quo if we compare the data whom the respondents found to cyberbully them or someone they met online. Seems respondents have been a participatory victim in this duo of victim-perpetrator complex. 48.8% did not share any such information which encouraged efforts to curb bullying online. Rest did not know whether they had done any such act (Table 1).

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6 Conclusion and Future Scope Survey done have showcased various perceptions undertaken by various part of the society and approved with the current NCRB 2021 crime data. Currently as per the data Maharashtra is leading state in this kind of crime being and reported, officially. Rajasthan is standing at the fourth position currently. However, with the everincreasing internet population which is having penetration of around 58% of total population as per the TRAI report. By 2025, this is expected to grow near 67%, which is going to be a staggering amount. Usage of various innovative technologies to curb this growing menace is required. Clear and inclusive definition of cyber bullying under the law is the need of the hour. There should be symbiotic association of law and technology in order to curb the problem of cyberbullying. Admissibility of evidence gained through digital forensic should have acceptance via court by setting undiluted standard operating procedures. Cyberbullying depends much on misinformation via cyberspace hence social media platforms should have fair information policy as this will help in tracking misinformation about any person in digital space. Use of Artificial Intelligence is increasingly proving its accuracy over the mentioned subject. It can be implemented in various manners but the accuracy and efficiency will only improve when it will be used in an optimal manner. Currently from psychologist point of view there are two different approaches preventive and interventional, through which a bully can be tackled. Similar approaches can be seen in various apps seen on Google Play store or Apple App store. However, rather than searching for the protagonist in this bully act, one approach can be strengthening and training the possible target or victim. This can be done with the help of various activities being targetedly given to understand and withstand the situation along with the sustainable environment for the possible victim. Currently with the advancement and inception of technologies involving Augmented and virtual reality like Metaverse are going to pose new threats in much different and complex manner. This needs to be taken in consideration in the forthcoming research and provide solutions for the same.

References 1. American Psychological Association (2022) Bullying. https://www.apa.org, https://www.apa. org/topics/bullying 2. All you need to know about Anti-Bullying laws in India. https://blog.ipleaders.in/anti-bullyinglaws/. Accessed 16 April 2022 3. Smith PK (ed) (2019) Making an impact on school bullying: interventions and recommendations. Routledge. 4. Dawkins (2011) A guide to cyber-bullying: an overview, harmful effects, awareness campaigns, popular culture, etc. webster’s digital services, p 128 5. Trolley BC, Hanel C (eds) (2010) Cyber kids, cyber bullying, cyber balance. Corwin Press 6. Walsh A (2012) E-Bullying: a parent’s guide to understanding and handling cyber-bullying (Beating the bullies book 4). Amazon Kindle, pp 0–30

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7. Toda Y, Oh I (2020) Tackling cyberbullying and related problems. Routledge 8. Shariff S, Hoff DL (2016) Cyber bullying: clarifying legal boundaries for school supervision in cyberspace 9. Krishna K (2018) Watch Out! No bullying. Scholastic India, pp 0–24 10. Nalini K (2018) Framework for detecting cyber bullying in twitter using bully latent dirichlet allocation with support vector machine. Bharathiar University, ShodhGanga, pp 1–139 11. Karthikeyan C (2022) Social and legislative issues in handling cyberbullying in India. In: Research anthology on combating cyber-aggression and online negativity. IGI Global, pp 352– 371 12. Rahul M, Choudhary S, Azhari M (2022) Crime against children in cyber space in India: a snapshot. Specialusis Ugdymas 1(43):4741–4751 13. National Crime Record Bureau (2022) Cyber Crime Report 2021. https://ncrb.gov.in/en/Crimein-India-2021. Accessed 28 Nov 2022

Hybrid Feature Extraction for Analysis of Network System Security—IDS T. P. Anish, C. Shanmuganathan, D. Dhinakaran, and V. Vinoth Kumar

Abstract Intrusion detection systems (IDSs) for computer networks play a crucial role in an organization’s performance. IDSs have been created and put into use over the years, utilizing a variety of methodologies to make sure that business networks are safe, dependable, and accessible. In this study, we concentrate on IDSs created by machine learning methods. IDSs based on machine learning (ML) techniques are proficient and reliable at spotting network assaults. However, as the data spaces increase, the effectiveness of these systems declines. Implementing a suitable removing features strategy that can eliminate some characteristics that have little bearing on categorization is essential. To examine the best characteristics in the data, this research suggested an efficient hybrid model that improves computation time and malware detection. This method addresses the problem of high negative result performance and low negative predictive value. Pre-processed data must first be correlated using the Gain Ratio and Co-Relation. Combining these approaches enables learning based on such an essential set of attributes and demonstrates improvement in accuracy and amount of temporal complexity. Keywords Intrusion detection systems · Computer networks · Machine learning · Feature extraction · Classification

T. P. Anish (B) Department of Computer Science and Engineering, R.M.K. College of Engineering and Technology, Chennai, India e-mail: [email protected] C. Shanmuganathan Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India D. Dhinakaran Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India V. Vinoth Kumar Department of Information Technology, Velammal Institute of Technology, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_3

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1 Introduction Security has become a top priority in many fields due to the extensive usage of computer networks. In addition, network security is becoming essential due to the expansion of online interaction and the accessibility of tools for network intrusion [1]. Unfortunately, the databases’ data is not appropriately protected by the security measures in place right now. Other security-enhancing technologies include firewalls, encryption, and authorization processes, although even these are vulnerable to assaults from hackers who exploit system weaknesses. A new intrusion detection system that combines a straightforward selection algorithm and SVM approach to protect data [2–5] has been designed and implemented in this research work to safeguard these networks from outside intrusions. Using the KDD Cup data set and data mining, massive datasets can be used to uncover hidden, reliable data. It is a formidable new technology that can help businesses concentrate on the most critical information in their data stores. Any form of data store can benefit from data mining techniques [6]. Algorithms and methods, however, could vary depending on the data format. The Internet has recently ingrained itself into daily life [7]. The current information computing system-based internets are vulnerable to attacks, resulting in various damages and significant losses. As a result, the significance of data security is rapidly changing. Creating defensive computer networks that are safe from unwanted access via disclosure, interruption, alteration, or demolition is the most fundamental purpose of network security. Additionally, network security reduces risks associated with primary security objectives, such as privacy, consistency, and reliability [8–10]. Information security is a significant issue that affects the entire world. The complexity and open-mindedness of the client/server methodology employed on the Internet have brought about enormous benefits for a progressive society. However, the rapidly expanding complexity and increasing availability of the connections not only encourage the extraction of information sharing data transmission vulnerabilities but also increase the risk of the already-existing information system [11]. The susceptibility of a network assault relies on how much information an intruder can afford to hack. An adversary’s intrusion into the communication network or computer server results in network dumps, malicious behavior, data theft, flooding, or process design denial [12–15]. Network systems are susceptible to attack and need an intelligent intrusion prevention mechanism to keep them safe. Data mining techniques are used to categorize the assault trends and identify intrusions in order to construct an effective IDS [16]. Data mining learning processes call for enormous amounts of training data, are pretty complex, and require analyzing how they compare to already available approaches. The Attribute Selection, Categorization, and Reliability approach consist of four steps for intrusion detection systems. Feature selection is a crucial preprocessing technique for data prediction and analysis. Feature selection solves the repetitive issues that lead to erroneous findings and reduced accuracy. It also improves categorization prediction accuracy by lowering training and time consumption complexity issues and improving stability near noise.

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Sort the feature selection techniques into filtering, wrapping, and hybrid approaches based on the feature subsets’ assessment [17]. The filter approach is computationally simple, quick, and scalable and self-determines the knowledge initiation procedure. These approaches are suitable for handling high-dimensional data because the filter technique employs the innate well-being of data as well as the target category on the path to be taught meant for feature selection. The wrapping ability uses a subset extractor to generate all likely subsets since a feature vector. In terms of processing, discretization, and computational burden, eliminating the attributes will improve IDS look [18]. A learning algorithm was then used to generate a classifier from the features of the subset. Building an effective detection system is a constant process that involves information security and process streams that look for intrusions. There are two methods for detecting intrusions: outlier and abuse or pattern detection [19]. The first technique examines the divergence from the typical behavior of the observed connected devices, whereas the second concerns the known attacks and signatures that have already been identified [20]. Vulnerability assessment can only detect known assaults but can identify fraudulent behavior on the Internet without raising many false alarms. On the other hand, an anomaly can identify both recognized and unidentified attacks [21]. This is critical since new attacks are continually being introduced into organizations. One technique for detecting anomalies is focused on machine learning and deep learning. We build a model to categorize network activity as usual or malicious after learning from a training sample [22]. This article presents a novel technique for identifying the most pertinent features that can help to create a compelling pattern recognition model for identifying assaults in the intrusion detection system (IDS).

2 Literature Survey Machine learning techniques for vulnerability scanning have been the subject of numerous studies—using various machine learning algorithms to construct secure information systems for security mechanisms. With the development of the internet, data security has recently been the focus of various research projects. Numerous kinds of literature on intrusion detection systems are available. IDSs are used to identify invader assaults. In designed to check DoS and DDoS attacks, Pushparaj et al. [8] provide a feature selection algorithm for intrusion detection systems (IDSs) employing Information Gain (IG) and Gain Ratio (GR) using the rated top 50% features. The top 50% of ranking IG and GR features are used to create subsets of features for the suggested system utilizing inclusion and merger procedures. Using a JRip classifier, the proposed technique is assessed and validated using the IoT-BoT and Knowledge discovery Cup 1999 data. In this research, Jaw et al. [9] introduced a potential hybrid feature selection (HFS) with various classifiers that effectively choose pertinent features and offer reliable

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attack categorization. To begin with, efficiently pick feature sets with a strong association by combining the advantages of CfsSubsetEval, genetic search, and a rulebased engine. This significantly reduced the computational burden and improved the sweeping generalization of learning algorithms—both of which are symmetry learning attributes. Additionally, they present an ensemble classifier called KODE that employs K-means, OneClass SVM, DBSCAN, and Assumption as an improved classifier that reliably distinguishes the asymmetric likelihood function between suspicious and innocent occurrences using an election system and the mean of probabilities. With the use of tenfold cross-validation, the CIC-IDS2017, NSL-KDD, UNSW-NB15 datasets, and numerous metrics, HFS-KODE produces outstanding results. Muhammad Hamdan et al. [10] use a novel hybrid feature selection algorithm to identify intrusions. In the built model, Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) methods are used in a novel way. They propose two new models for selecting features and intrusion detection: PSO-GWO-NB and PSOGWO-ANN. PSO and GWO demonstrate emerging feature extraction results for many purposes. In this study, the elements for malware detection were chosen using PSO and GWO. A novel emergence feature representation is created by employing the intersection of (PSO and GWO) features. Additionally, this study evaluates and names the Most Commonly Repeated Characteristics of (PSO and GWO) (MRF). The user might define the number of repetitions for this study’s PSO and GWO to run for. The chosen feature set is preserved, and each attribute selection method operates separately. The following phase incorporates testing of PSO features, features at the convergence of (PSO and GWO), and MRF features. The UNSW-NB15 dataset is utilized in this study for evaluation purposes. Additionally, two classifiers—Naive Bayesian (NB) and Neural Nets (ANN)— were used in the tests. The findings indicate that PSO and GWO are excellent choices for intrusion detection characteristics. Additionally, the intersection of (PSO and GWO) features produces an emergent result with the fewest possible features. Pham et al. [11] aim to enhance IDS performance through primarily linked feature selection. With the tree-based methods serving as the primary classifier, the composite models were created using the two multiple classifiers, bagging and promoting. After that, the NSL-KDD dataset was used to assess the proposed models. When using the subset of 35 chosen features, the simulation results reveal that the bagging ensembles model with J48 as the base classifier generated satisfactory performance in terms of both the classification results and FAR. Mehedi Hasan et al. [12] emphasize the two-step method of feature selection based on Random Forest. The initial search strategy for the second step, which produces the final set of features for categorization and interpretation, is guided by selecting the characteristics with potentially higher relevance scores in the first stage. The KDD’99 intrusion detection datasets, which are based on the DARPA 98 dataset and offer a classifier for research teams in the intrusion detection field, demonstrate this technique’s usefulness. However, as previously noted, the massive number of duplicate entries found in the KDD’99 data collection is a significant

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flaw. To prevent the classification and feature selection processes from favoring more frequent records, they created the data collection RRE-KDD by removing redundant records from the KDD’99 training and testing sets dataset. For training purposes, the KDD99Train+ and KDD99Test+ datasets are included in this RRE-KDD. Opeyemi et al. [13] Prior to classification, they present a feature selection approach that combines the Gain ratio, Chi-squared, and ReliefF (triple-filter) filter methods in a cluster-based heterogeneous WSN. By removing 14 critical characteristics from the dataset’s original 41 features, the classification performance will be improved while the method’s sophistication will decrease. Performance is assessed by taking into account detection rate, accuracy, and the false alarm rate using an intrusion detection feature set called NSL-KDD. Results demonstrate that, compared to other filtering approaches, our suggested method can successfully minimize the number of characteristics with a higher accuracy rate and detection accuracy. Additionally, compared to the existing filter selection approaches, the suggested feature extraction tends to lower the overall energy used by SNs during vulnerability scanning, prolonging the network lifetime and usefulness for an acceptable amount of time.

3 System Model Data processing is the main focus of the first block. The term “data engineering” is frequently used to describe this procedure. For the process of learning to be successful, this phase is essential. There are three stages to information processing: cleansing, feature standardization, and extraction of features. First, a filtration strategy creates feature significance ratings in the attribute selection process. After selecting the necessary feature vector, the model is trained well using the training data set [23]. The validation set is to verify a trained model. The validated model is next tested using the testing dataset. The process is repeated until a tweaked model that fits the data is discovered. Figure 1 demonstrates the architecture of the proposed representation.

3.1 Feature Normalization The learning process using machine learning techniques is impacted by the high numeric values of many features. High-dimensional dataset training necessitates extensive computational resources. Data is frequently scaled using techniques like Max normalizing, normalization, Numeric scaling, and Min–Max climbing to address these challenges. The program is commonly taken into account while deciding which approach to use. As part of our data preprocessing step, we employ the Min–Max scaling (Eq. 1).

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Fig. 1 Architecture of the proposed approach

Min − Max scaling F =

MM − MMmin MMmax − MMmin

(1)

Given the dataset with an vector input which is represented by MM(fs1 , …, fsn ), 1 < n < X, where X is the sum of instances.

3.2 Feature Selection The suggested model combines Gain Ratio and Correlation with correlation-based particle swarm. Combining these methods offers especially among university on a significant fraction of attributes [24]. 1. Gain Ratio (GR) based feature selection In a decision tree, terminus nodes provide the actual value of features, while nonterminal nodes test more aspects of feature value. When choosing elements by Gini index, large concentrations characteristics are the best. Information Gain is better than

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Gain Ratio. The most effective way to reduce bias is to choose the subset of features based on their weight. The collected characteristics information ratio defines the Gain Ratio. Features are partitioned into Vp partitions using this method and training data [25]. Allow DI to be made up of feature information and m division labels. Equation 2 specifies how traditional feature knowledge classifies a demanding set of features. ( ) ΣVp ( Di ) Di log2 DivideInfo (DI) − i=1 D D

(2)

where Di are the point of view of a group of features that belong to labels and are predictable by Di /D. Allowing for separate weights for characteristics DI Let Dij be the quantity of label-related characteristics in a subset Dj . It contains D-specific elements with DI weight. Observable feature data is provided by DI that is based on the subsets it has created. GainRatio(Gr) =

Gain(Gr) DivideInfo

(3)

Gain (Gr) = I(D) − E is the training knowledge that would result from splitting on DI (DI). Present the characteristic in rank construct in addition to all those values by dividing the labeled training data DS into segments based on the results of a test on the characteristics of DI. The characteristics of which have been divided into qualities and given a high weight. Gain ratio (Gr) is define as Gain Ratio (DI) = Gain (DI)/DivideInfoA (DS) (4) The features of which have highly weighted that feature preferred by splitting attributes. 2. Correlation based feature selection (CBFS) Every single factor is identified concerning other features, and a subset of attribute weights can be observed through correlation. The correlation coefficient and estimated inter-correlation are used to analyze the relationship between the subset of characteristics and labels [26]. The correlation between aspect and class labeling leads to the development of a set of attributes, and CBFS reduces inter-correlation. Ki Czi CBFS = √ Ki + Ki (Ki − 1)Cii

(5)

where czi is the average of the associations between the population of characteristic and the label value, rzc is the correlation between the gathered subsets of features and the label value, ki is the numeric of the subset features, and is the level with respect across subset features. Gain Ratio (GR) and Correlation (CBFS) results allowing filter technique predicated ordering.

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Algorithm - Proposed Model Input = Function Fn (Intrusion dataset) Output = Intrusion detection Reduce Fn features using Feature selection approach - Gain Ratio & based feature selection. Select the feature from the given input Using filter method, Initialize - particle position. Randomly select the particle using random selection. Do For each particle Calculate the fitness value. If (fitness rate is superior than existing Fbest) select finest fitness rate of the whole particles. Compute the particle velocity. Revise the updated particle position. END Select feature Fb Provide Fn to classifier - trained data set Calculate ID

Correlation

4 Performance Evaluation Our goal is to create a feature-selected classifier that may provide the highest accuracy for each type of assault pattern. However, the various fully convolutional models must be deliberately built to have the best generalization ability for classification [27]. We conducted our tests using an Intel 2.50 GHz processor and 16 GB of RAM in the Windows 8 operating system. Additionally, we used the WEKA machine learning algorithm to classify the KDD dataset into five categories. The intrusion detection system: IDS for the NSL-KDD dataset uses the suggested strategy. Furthermore, the outcomes are disclosed [28–32], without feature choice. Every time a region is examined, it is critical to assess the classification technique and gauge the algorithm’s effectiveness. To evaluate the categorization algorithms, we looked at the four metrics listed below (Fig. 2; Table 1): (1) Accuracy: The proportion of correctly predicted events. Accuracy =

(True Positive + True Negative) (True Positive + True Negative + False Negative + False Positive) (6)

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95 Performance

90 85 80 75 70 65

ANN

LR

KNN

SVM

Approaches Accuracy

Precision

Random RBF Proposed forest classifier

Recall

FI-Score

Fig. 2 Performance of different Approaches

Table 1 Performance of different approaches with feature selection Approaches

Accuracy

Precision

Recall

FI-score

ANN

80

83

77

70

LR

82

90

78

74

KNN

83

83

81

79

SVM

86

91

84

75

Random forest

89

82

85

82

RBF classifier

84

87

87

79

Hybrid Feature Extraction

91

93

89

83

(2) Precision: The proportion of accurately classified confirmed samples to all positive instances. Precision =

(True Positive) (True Positive + False Positive)

(7)

(3) Recall: The proportion of confirmed cases that have been correctly categorized as positive. Recall =

(True Positive) (True Positive + False Negative)

(8)

(4) F1-Score: Indicates how well memory and precision are balanced. It calculates the percentage of positive cases that were mistakenly labeled as negative. F1 − Score =

(Recall ∗ Precision) ∗2 (Recall + Precision)

(9)

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5 Conclusion This research focuses on the intrusion detection systems’ (IDS) feature selection process. A novel approach is suggested to simplify the IDS and boost the effectiveness of a machine learning (ML) model. The system promises to block a variety of infiltration attempts and can be trained to block fresh and novel attempts. To examine the best characteristics in the data, this research suggested an efficient hybrid model that improves computation time and malware detection. A dynamic field closely related to data mining and other data processing technologies, feature reduction strategies incorporates the data mining methodology to increase classification efficiency. This method addresses the problem of high negative result performance and low negative predictive value. In comparison to other conventional methods, the proposed association with Gain Ratio and Co-Relation methods offers feature reduction that enhances training effectiveness and achieves correctness. In the future, we will run more tests on the proposed system and discover various levels of accuracy. In order to increase IDS precision, we plan to enhance the optimization algorithms.

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Stranger Trust Architecture: An Advancement to Zero Trust Architecture Arjun Choudhary , Arun Chahar, Aditi Sharma, and Ashish Tripathi

Abstract Security in ever changing environment is the need of the hour and no business or establishment can survive without a proper and suitable security mechanism. There have been several mechanisms for attending the needs of different sectors. In recent years Zero Trust Architecture has emerged as one of the promising security mechanisms to ensure the utmost privacy and setback resistant authentication mechanism. In this research paper we have tried to propose an advanced level of authentication mechanism known as the stranger trust mechanism. Keywords Security · Privacy · Zero trust architecture · Stranger trust architecture · Authentication · Authorization

1 Why a New Method? This method was proposed as, in Zero Trust Architecture (ZTA) at some point which still continues to provide a certain amount of trust after authentication and authorization of a user/service. Although it is assumed that the attacker is present in the environment and nothing can be secured, the zero trust falls a bit short on this part as there is still an implicit trust zone present. A. Choudhary (B) · A. Chahar Center for Cyber Security, Security and Criminal Justice, Sardar Patel University of Police, Jodhpur, India e-mail: [email protected]; [email protected] A. Chahar e-mail: [email protected] A. Sharma School of Engineering and Digital Sciences, Nazarbayev University, Asthana, Kazakhstan e-mail: [email protected] A. Tripathi Department of Information Technology, G. L. Bajaj Institute of Technology and Management, Greater Noida, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_4

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Therefore, in the Stranger Trust Architecture (STA, which also can be named as ZTA + or ZTA Advanced), it is assumed a worst-case scenario that each component in an environment is compromised due to which one cannot grant trust to anyone or any component, even if one has to do the authentication and authorization. Each component is treated as complete stranger and with suspicion that it may initiate attack at any time. So, to secure the digital infrastructure efences are kept up and every single component is monitored, even after authorization. The implicit trust zones [3] are shrunk in ZTA but in STA (Stranger Trust Architecture), the implicit trust zones are completely absent and the concept of familiarity and suspicion are added. Familiarity is how much the environment components are familiar to the subject and can recognize the subject. If authentication is granted then it is assumed that the system is familiar with it, but it does not trust the subject at all. Suspicion is maintained and it is always assumed that an adversary has been granted access. Based on the above concepts two indexes are added for each component. Note, that by the term component in cloud environment we mean the user, application, service, etc. and resource is the data, as mentioned in the NIST ZTA paper [1]. Each component has two parameters associated with it, the Familiarity Index (FI) and Suspicion Index (SI). Before communicating with another component, it will always look for FI and SI of other components which will be further discussed in coming sections. Only after these are accounted for, access to the resource is provided for even a single action.

2 Previous Works NIST has published a paper about Zero Trust Architecture. The paper on Zero Trust Architecture was published in a special publication SP 800–207 by National Institute of Standards and Technology (NIST) on August, 2020 [1]. According to NIST the Zero Trust Architecture is defined as follows. Zero Trust (ZT) provides a collection of concepts and ideas designed to minimize uncertainty in enforcing accurate, least privilege per-request access decisions in information systems and services in the face of a network compromised. Zero Trust Architecture (ZTA) is an enterprise’s cybersecurity plan that utilizes zero trust concepts and encompasses component relationships, workflow planning, and access policies. Therefore, a zero-trust enterprise is the network infrastructure (physical and virtual) and operational policies that are in place for an enterprise as a product of zero trust architecture plan.

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3 Basics of Stranger Trust Model Initially Zero Trust Access model is being mentioned. According to this model, whenever a subject wants to access the enterprise resource, the access is granted via policy decision point (PDP) and policy enforcement point (PEP). The authentication and authorization process are done and trust is granted to the subject. There is an implicit trust zone which trusts all the entities present after these processes are done (Fig. 1). As shown above it can be seen that after going through PDP/PEP Implicit Trust Zone is present for access of resources. But, in Stranger Trust Access this zone is removed and as the term itself suggests we are trusting subjects as if we are trusting a stranger (Fig. 2). In Stranger Trust Access the PDP/PEP carries out the regular authentication and authorization Implicit Trust Zone is absent. Nothing is assumed valid and still has a lot suspicion. The Untrusted Zone is replaced by the Stranger Zone. This zone feedbacks the FI and TI values to the PDP/PEP which in turn can then decide whether to grant access or not. The Familiarity Index (FI) and Suspicion Index (SI) Calculator block does the generation of FI and SI assigns it to the subject and every other component. Due to trust being absent we assume that the subject is an adversary and this FI SI Calculator block inputs these values as an input. FI is calculated based on the services used by the subject, so from their own FI and SI the services are able to recognize that the particular subject is regularly

Fig. 1 Zero trust architecture access mechanism

Fig. 2 Stranger trust architecture access mechanism

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using that service. It can be calculated via Machine Learning models. As an analogy, each component is a neighbor, if there is an activity between any two the others are although not aware what happened between them but are familiar amongst themselves and have an indirect relationship. This secondary and non-direct connectivity and recognition is then expressed in terms of Familiarity Index (FI). So other components are also aware of their neighbors. High FI means more familiarity and recognizable. SI is calculated based on the activity of the subject. In addition to the continuous monitoring in usual ZTA, in STA the SI would allow each neighbor to monitor the neighborhood. Similar to FI, it can also be calculated using ML models. As an analogy, if there is activity going on in the neighborhood as one of the parties is a complete stranger and the other being the neighbor. And they are doing something unusual then the surrounding neighbors are alerted and maintain suspicion. Similarly, the SI also makes use of the secondary connectivity and alerts other connected neighbors to increase SI and alert the monitoring service. The monitoring service then can-do comprehensive analysis and detection. High SI would mean a higher level of threat.

4 Tenets of Stranger Trust The basic tenets of ZTA as defined by NIST [1]: 1. 2. 3. 4.

All data sources and computing services are considered resources. All communication is secured regardless of location. Access to individual enterprise resources is granted on per-session basis. Access to resources is determined by dynamic policy—including the observable state of client identity, application/service, and requesting asset—and may include other behavioral and environmental attributes. 5. The enterprise monitors and measures the integrity and security posture of all owned and associated assets. 6. All resources authentication and authorization are dynamic and strictly enforced before access is allowed. 7. The enterprise collects as much information as possible about the current state of assets, network infrastructure and communications and uses it to improve its security posture. In addition to these tenets of ZTA, for STA we must make sure that: 1. A complete neighborhood mapping is done based on the collected data: Each neighboring asset only knows a certain number of assets based on direct and secondary connections. Any other assets are termed as strangers and FI is reduced. Only in a certain area that the particular asset has high FI. The complete map should be maintained centrally to get the bigger picture. In a way we are making an asset a smart asset.

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2. Comprehensive central and distributed monitoring must be carried out: Each asset monitors other neighboring assets and knows when its neighboring asset is in connection with the non-neighbor asset or an asset with low familiarity or low FI. This leads to increasing the suspicion of being breached and increasing the SI. This individual monitoring is done by all the neighbors at the same time, and then notified for central monitoring. 3. A standard for the proprietary format must be defined: Interoperability becomes a major issue when switching from one provider to another when each have their own formats. In STA, a strict standard is defined which all providers must have to follow to make it easier for enterprises when they are migrating their resources. Threats Resolution of ZTA in STA 1. Subversion of ZTA Decision Process: Policy Engine (PE) [4] and Policy Administrator (PA) [5] are the key components of an entire enterprise in the ZTA. And PA and PE each have their associated risks. With compromised PE rules any unapproved changes can be made or with compromised PA rules it can allow access to resources which are otherwise not allowed. In STA, with the help of FI and SI any unapproved changes are immediately blocked under comprehensive monitoring and access to resources is inherently blocked. 2. Denial-of-Service or Network Disruption: The STA is very resilient in this regard as due to suspicious number of requests of activity which are immediately contained. So, any suspicious activity cannot occur and will be notified and immediately blocked. Even if the attacker gains access through the PE, the FI and SI will immediately cause the attacker’s access to be revoked and recorded the relevant information for future references so that such types of attacks are easily identified. So, in case of compromise of PE or PA the FI and SI are fully functional as they are independent of them, hence giving enough time to regain access to PA and PE. 3. Stolen Credentials/Insider Threat: The ZTA uses Trust Algorithm to ultimately decide whether or not to deny access to resources which means if the account is compromised, then the attacker can gain access to the resources. However, unauthorized access to other resources is prevented but the compromised account’s resources access is still available. In STA, there is no Trust Algorithm but FI and SI. Suppose a subject uploads a random number of resources a set value of FI and SI is calculated. And based on every activity it is continuously updated. But when the attacker gains access, he will not follow the subject behavioral patterns and will cause changes in SI, which in turn will focus on current account and resource and even deny access to resources. Malicious Insiders can be further mitigated with the Multi User Authentication Policy mentioned in the STA Features section. 4. Visibility on the Network: How to assess and monitor encrypted network packets has become a key question. The attacker may as well be using the encrypted packets on network. The Machine Learning approach used by ZTE is sufficient enough. In STA the network mapping is already done via the neighborhood method, so with ML it becomes easier to identify an attacker.

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5. Storage of System and Network Information: For the threat of the analysis component, we can use a new method proposed in this paper itself in section STA Features named Procedural NGFW Maze generation in DMZ, where we can add DMZ and surround the analysis component with 1-D, 2-D or 3-D maze of firewalls. This will make it very difficult to find the correct path to the analysis component. In addition, the attacker can be misguided and looped through the maze itself with new firewalls constantly being generated giving illusion of infinite firewalls to attacker. 6. Reliance on Proprietary Data Formats or Solutions: All cloud service providers use their own proprietary format, which causes interoperability issues. An Inherent feature of STA itself is defining a strict standard of proprietary format. If other cloud service providers themselves switch over to the common standard of such format, then the enterprise costs for migration will drastically go down as enterprises would only have to change their provider when they want and don’t have to worry about anything else. 7. Use of Non-Person Entities (NPE) in ZTA Administration: The use of ML and AI does pose the challenge of false positives and false negatives when using automated technology. In STA, such issue is completely minimized due to 2 factors–(i) Central monitoring which does continuous monitoring of all the components, (ii) FI and SI updates and distributed monitoring. Hence there is reduced and very low possibility of the attacker to be successful.

5 STA Features For STA the following new features are also proposed to increase the security.

5.1 Multi User Authentication For the enterprises it is very common for attackers getting login credentials or a malicious insider or an unknowing employee having no idea being infected with malware. It only takes one enterprise account for an attacker to do huge amount of damage. In this proposed method it overcomes this issue.

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In Multi User Authentication (MUA), the enterprise entities at the higher level of hierarchy which have more privileges and permissions associated with their accounts require authentication from random employees and with their confirmation with their original login credentials can they be authenticated. In the diagram we took Administrator as an example. When administrator wants to get authenticated, he inputs the login credentials and a confirmation status notification is sent to any number of employees randomly selected. The administrator then has to communicate with the employees that they want to login and want them to agree to confirmation status. These employees then must authenticate and agree to the administrator login. After each employee confirmation status is approved it is then sent as an input to the Authenticator element. After that the authentication is carried out and only then the Administrator is authenticated. The selection of employees or only whitelisted accounts etc., are all upon the enterprise. This is done so that in the event of Administrator compromise the employees will know that the administrator has not logged in but the confirmation status arrived which means the administrator account is compromised but the attacker is not able to authenticate due to this method. MUA can be implemented for all the accounts which have a certain level of access to resources. In this manner enterprises can drastically reduce the account theft related attacks. Malicious Administrator is completely eliminated. Malicious insider related attacks risks are also eliminated. This method may only fail unless there is a malicious administrator and all the selected accounts for confirmation are also malicious insiders.

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Procedural NGFW Maze Generation in DMZ DMZ [6] is generally used for building semi-secure network to secure internal network. Whereas, Next Generation Firewall also comes with its own perks such as Intrusion Prevention System (IPS) [7], Deep Packet Inspection (DPI) and Application Control. Procedural generation concept is generally used in game development for generating random content with very small initial data. Suppose we have to protect our Analysis Component which determines about giving access from attacker. We add a DMZ and in between the DMZ and the Analysis Component (AC) this feature comes into picture. A procedural algorithm for the virtual NGFW generation is added which can generate any number of firewalls between the DMZ and AC. In the event when the attacker was able to breakthrough even through the DMZ, the procedurally generated firewalls can severely impede the progress of the attacker. These firewalls are randomly generated so each time their parameters would be different which will greatly confuse the attacker. 1-D generation is linear and any number of firewalls are generated.

In 2-D generation we can create a 2-D array of firewalls and the correct path is to pass through it like a maze. It increases the complexity for the attacker as compared to 1-D generation. Due to being a maze-like structure this method uses the term maze.

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In 3-D generation it is just an extension of 2-D generation which tremendously increases the complexity of the firewall path and is very difficult for attack to breach through without being able to find the correct path.

6 Conclusion A new architecture named Stranger Trust Architecture (STA) was proposed which is based on Zero Trust Architecture (ZTA). It mainly dealt with the removal of implicit trust zones and never trusting any subject, not even the authenticated one. In the near future as NGFW and Firewall-as-a-services are emerging it would become standard in DMZ for isolating the critical infrastructure. Continuous Monitoring and Machine Learning algorithm based trained models will play an important role. Multi-layer approach is used so procedurally generated virtual blockades and checks will be extensively used in cloud. And finally, even the subject cannot be trusted due to compromised accounts attacks, so we may move towards the proposed multi user authenticated methods for enterprises. The role of the trust will be completely diminished.

References 1. Rose S, Borchert O, Mitchell S, Connelly S (2020) Zero trust architecture, special publication (NIST SP), Natl Inst Stand Technol 2. San, Kalyar Myo (2019) Toward cloud network infrastructure approach: Service and security perspective, https://d1wqtxts1xzle7.cloudfront.net/60493825/424_Toward_Cloud_Network_ Infrastructure_Approach_Service_and_Security_Perspective20190905-43957-8kza5s-withcover-page-v2.pdf 3. Xuan ZG, Li Z, Liu JG (2011) Information filtering via implicit Trust-based network. arXiv preprint arXiv:1112.2388.

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4. Sundaram B, Chapman BM (2001) Policy engine: a framework for authorization, accounting policy specification and evaluation in grids. In Int Work Grid Comput. pp 145–153. Springer, Berlin, Heidelberg 5. Burns J, Cheng A, Gurung P, Rajagopalan S, Rao P, Rosenbluth D, Martin DM (2001) Automatic management of network security policy. In Proceedings DARPA Information Survivability Conference and Exposition II. DISCEX’01 2, pp 12–26. IEEE 6. Dadheech K, Choudhary A, Bhatia G (2018) De-militarized zone: a next level to network security. In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp 595–600). IEEE 7. Choudhary A, Bhadada R (2022) Insider threat detection and cloud computing. In Adv Data Inf Sci (pp 81–90). Springer, Singapore

Genetic Algorithm Optimized SVM for DoS Attack Detection in VANETs Ila Naqvi , Alka Chaudhary , and Anil Kumar

Abstract A VANET is a collection of wireless vehicle nodes that may connect with one another without the need of fixed infrastructure or centralized management. Vehicular ad hoc networks (VANETs) function in a dynamic and unpredictably changing environment that brings numerous potential security risks. One of the types of attacks that affect VANETs the most is the Denial of Service (DoS) attack. Additionally, VANETs can’t be secured by following the conventional approaches to protecting wired or wireless networks because of the ever changing network topology. Because preventative methods are insufficient, using an intrusion detection system (IDS) is crucial to the VANET’s defense. In this paper, a new intrusion detection system has been proposed by using an Artificial Neural Network-Fitness function based Genetic Algorithm and Support Vector Machines (SVM) for vehicular ad hoc networks to detect the denial-of-service attack. Keywords VANET security · Intrusion detection · Vehicular network security · Denial of service attack · Genetic algorithm · Support vector machines

1 Introduction The VANET network is a novel piece of technology that will form the foundation of future intelligent transportation systems. Its objective is to improve the quality of the driving experience by enhancing the vehicle environment. In addition to this, it enhances the level of safety enjoyed by motorists and vehicles when they are driving. Due to the effects of congestion and an increased risk of collision, today’s roadways are growing more dangerous as the number of vehicles on the roads continues to rise. It is essential that messages regarding safety be delivered to automobiles in a timely I. Naqvi (B) · A. Chaudhary AIIT, Amity University, Sector-125, Noida, Uttar Pradesh, India e-mail: [email protected] A. Kumar DIT University, Dehradun, Uttarakhand, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_5

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and accurate manner. Since the availability of the network is so crucial whenever one node provides any information that could potentially save the lives of other nodes, it is essential that network availability should always be always maintained in order to ensure safety. In this context, the availability of the network is vulnerable to a wide variety of attacks. DOS attacks are one of the many security concerns that VANET inherited from MANET despite having a distinct design. This is because VANET is a subclass of MANET. The Denial of Service (DoS) attacks that prohibit users from receiving the appropriate information at the appropriate time are the primary emphasis of this study. The VANET defense relies heavily on the use of an Intrusion Detection System (IDS) [1] to defend the network from this type of attack. In this study, the Denial of Service (DOS) attack on network availability is discussed and its level of severity in a VANET context is elaborated upon throughout the course of this paper. A new IDS for detecting Denial-of-Service (DoS) attacks in vehicular ad hoc networks has been developed by integrating genetic algorithms and Support Vector Machine (SVM).

2 Literature Review In Vehicular networks, RSUs are now connected to more than 250 million vehicles worldwide. One in five vehicles can communicate wirelessly, making self-driving and semi-autonomous vehicles crucial components of the Internet of Things [2]. A hybrid SVM and a feed-forward neural network-based IDS was proposed by the authors in [3]. During the simulation process, a trace file is created. From this file, features are extracted using the proportional overlapping score and classified using fuzzy sets. The intrusion is discovered using the Support Vector Machine and Feed Forward Neural Network. The authors examined the effectiveness of the built system in both instances after studying both the system’s normal and aberrant behaviors. The findings indicate that the created IDS operates admirably in both scenarios. Authors in [4] suggested an IDS that combined genetic algorithms with artificial neural networks. For optimization and feature categorization, respectively, the Artificial Neural Network and Genetic Algorithm are used. The suggested technique outperformed existing algorithms in terms of accuracy, false alert rate, throughput, and precision when the number of hostile nodes was high. The Dolphin Swarm technique is used to optimize the SVM at the foundation of the IDS presented in [5]. The authors also suggest a decision-making algorithm, referred to as the Hybrid Fuzzy Multi-Criteria Decision-Making Algorithm. In order to choose the cluster heads, this approach uses TOPSIS and the Fuzzy Analytic Hierarchy Process. In order to identify any harmful vehicles, the cluster chiefs inspect the vehicles in their respective clusters. A malicious vehicle is prevented from using any network services if it is found. Every car in the network is screened this way for signs of harmful behavior. The Dolphin Swarm algorithm has been optimized utilizing the Supervised Machine Learning (SVM) technique to find hostile vehicles.

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A hybrid intrusion-detection technique for VANETs was presented by Adhikary et al. [6] to identify DDoS (Distributed Denial of Service) assaults. For DDoS detection, the method uses the RBFDot and AnovaDot SVM kernel functions. The article made advantage of a number of features, such as throughput, latency, jitter, collision, and packet drop. The performance of the suggested hybrid approach is compared in the paper to the single SVM methods used by AnovaDot and RBFDot. For the purpose of identifying Distributed Denial of Service (DDoS) attacks. Gao et al. [7] designed a distributed network intrusion detection system. Micro-batch data processing is used to acquire traffic feature data. The system uses a classification method based on Random Forest during the detection phase, and HDFS was utilized to store the information about the suspect nodes. For the purpose of detecting Denial of Service (DoS) in vehicle ad hoc networks, Ali Alheeti and McDonald-Maier in [A] suggested a hybrid detection system that makes use of back propagation neural networks. Authors in [8] describe how writers used deep neural networks (DNN) to create an intrusion detection system to safeguard vehicle communication systems. The suggested method mainly relies on communication characteristics that are gleaned from packets. The security system is crucial in protecting the data received to and transferred among the vehicles inside the radio coverage area. Naqvi et al. [9] produced a comparison of several approaches that reflects the current situation and open challenges of vehicle security and privacy. The architecture, issues, and needs of an intrusion detection system in VANETs are reviewed here.

3 Problem Statement: DoS Attack In a wireless setting, an attacker will often launch an attack on the communication medium in order to either cause a channel jam or produce other difficulties for the nodes in their attempts to access the network. The primary objective is to stop the authentic nodes from using the network resources and gaining access to the network services. The attack could lead to the complete devastation of the nodes and cause the resources of the network to become exhausted. In the end, the networks will no longer be accessible to any node. In VANET, where information that is both seamless and life-critical must reach its intended destination in a secure and timely manner. In DoS, there are three methods that attackers might use to carry out the attacks, and they are the jamming of communication channels, overloading of networks, and packet dropping [10].

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Fig. 1 Flooding in V2V communication

Fig. 2 Flooding in V2I communication

3.1 Overwhelming the Node’s Resources The objective of this DOS basic level attack is to exhaust the resources of the node to such an extent that the node is unable to carry out other operations that are both required and crucial. The verification of the messages causes the node to be constantly occupied and uses up all of its available resources. This attack can be carried out both on vehicle node or Road side unit. When the targeted node or targeted RSU is continually busy verifying the messages, any other nodes that want to communicate with the target will not be able to get any response from it, resulting in the inaccessibility of the service. As a result, sharing vital information in this circumstance becomes extremely difficult [10]. Figure 1 and Fig. 2 show the overwhelming of node’s resources in V2V and V2I communications respectively.

3.2 Channel Jamming This is a more advanced form of the DoS attack, in which the attacker causes jamming on the channel, preventing other users from connecting to the network.

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Fig. 3 Channel jamming in V2V

Fig. 4 Channel jamming in V2I

As seen in Fig. 3, an attacker will send a channel with a high frequency in order to disrupt any communication taking place between nodes inside a domain. Due to the attack, these nodes are unable in transmitting or receiving messages inside that domain, which means that services are not now available within that domain. Once a node has exited the attacker’s domain, and only then, can it transmit and/or receive messages from other nodes. The following step of the attack is to disrupt the communication link that exists among the nodes and the infrastructure. The scenario depicted in Fig. 4 depicts the situation in which the attacker initiates an attack near the infrastructure in order to jam out the channel, which ultimately results in the failure of the network. Because of this, it is unable to transmit or receive messages to or from other nodes, and any attempt to do so would fail because the network would not be available [10].

4 Dataset The VeReMi Extension dataset [11] was used for this study. This dataset is freely accessible to researchers online. The dataset was created using VEINS and LuST (Version 2). The VeReMi Extension dataset includes the message logs for each car,

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Fig. 5 VeReMi extension dataset properties [11]

each of which includes BSM messages (labelled as type = 3) received over DSRC from other vehicles as well as GPS data (labelled as type = 2) about the local vehicle. It achieves two important objectives: first, it serves as a benchmark to assess how effectively misbehavior detection methods perform at the city level; and second, it conserves a sizable amount of computing resources. The properties of the VeReMi Extension dataset are briefly summarized in Fig. 5.

5 Proposed System The proposed system consists of following three phases: • Data pre-processing • Feature selection using Genetic algorithm • Classification using SVM.

5.1 Data Pre-processing The VeReMi extension dataset is provided in JSON file format. In order to use it in our system, the data files need to be converted into .csv format. We used Gigasheet.co to convert the data files from .json to .csv format. We fetched 33 features from the VeReMi Extension dataset. The dataset consists of 6000 message logs. Thus, the dimensionality of the dataset is 6000 × 33. Systems for pattern or image recognition could be directly challenged by high dimensional feature sets. In other words, having large number of features might sometimes decrease the accuracy rate of detection system because some of the features might be redundant or otherwise useless [12]. To maintain the best combination and reach the highest level of accuracy, several combinatorial sets of features should be obtained.

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5.2 Feature Selection Using Genetic Algorithm The SVM model consists of four hyper-parameters: kernel function, kernel function coefficient, penalty coefficient, and polynomial degree [13]. There is no question that the accuracy of the intrusion detection model will be impacted in a variety of ways by the parameter choices. As a result, the evolutionary algorithm is put to use in order to carry out an automatic selection of the SVM classifier’s most suitable hyperparameters. The number of features required by the SVM in this study will be reduced using a GA-based feature selection. A feature subset selection, F Sel is a process that maps an input p-dimensional feature space to an output q-dimensional feature space. FSel : R n× p → R n×q

(1)

where p ≥ q and m, q ∈ Z + , Rn×p is any matrix or dataset containing the original feature set having n observations with p features, Rn×p is the reduced q feature set containing n observations in the sub-set selection. Reproduction, genetic crossover, and mutation are the three primary functions that are carried out by the genetic algorithm. The chromosomes in the present generation that have the highest fitness values are the ones that the reproduction operator copies into the next generation. It is a highly specialized strategy with the overarching objective of improving the quality of life for future generations by capitalizing on the superior chromosomes of the current population. The crossover operator is of the utmost significance. In order to generate two off-springs, it first chooses the chromosomes that have the best fitness values to serve as the parents. After reaching a certain crossover point, it then swaps the sections of the parents’ chromosomes. A gene value of “1” on the binary chromosome employed in this study denotes the selection of the specific characteristic denoted by the position of the “1.” If it reads “0,” the feature is not considered when evaluating the chromosome in question. The n children (Elitism of size n) with highest fitness are chosen to include in the following generation after the chromosomes have been sorted [14]. Through Algorithm 1, the fitness evaluation is carried out. By arbitrarily altering the expression of some genes on a chromosome, the mutation operator can prevent the formation of a local optimal solution while also ensuring the diversification of potential solutions. The three operators are iterated over and over again for a considerable number of generations till they reach the termination condition. When the optimum values for the hyperparameters have been identified, the data is passed to the SVM classifier for training and testing. The Kernel function used in the SVM classifier is the Gaussian kernel function [15] having box constraint level 1 with manual kernel scale 1.2.

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5.3 Fitness Function In order for the GA to select a subset of features, a fitness function—which serves as a driver for the GA—must first be created. This fitness function should evaluate the discriminative capacity of each subset of features. The ANN-based fitness function [16] is used to assess how each chromosome in the population contributes to the overall fitness of the population (see Algorithm 1). The individuals (various combinations of features) in the present population are assessed as the GA iterates, and their fitness is ranked according to the accuracy of the ANN-based classification. Those who are physically fitter have a greater probability of living into the following generation. The iterations that are part of the process of running the GA ensure that the error rate is reduced and the individual with the highest accuracy (fitness value) gets selected. This is because GA calculates the accuracy for every chromosome and the individual with the highest accuracy rate is picked by GA in the end. FitFn =

N Σ xi ∗ 100 N 1

(2)

where, x = accuracy of ANN N = number of features Algorithm 1

Fitness Function Evaluation () 1: procedure Fit() 2: for i=1 to k: 3: ANN_Accuracy(i)← ClassifierNN(Features, Labels) 4: meanAccuracy← mean(ANN_Accuracy)*100 5: Return meanAccuracy 6: end procedure

6 Results In order to determine the accuracy of our hybrid of Genetic algorithm, ANN and SVM, we conducted the study in two steps. In the first step, we passed the whole data after Data pre-processing step to SVM and recorded the results. In the second step, we used ANN fitness function based genetic algorithm optimized SVM and recorded the results.

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Table 1 Results with and without Genetic Algorithm Without GA

With GA

Fine Gaussian SVM (%)

Medium Gaussian SVM (%)

Coarse Gaussian SVM (%)

Fine Gaussian SVM (%)

Medium Gaussian SVM (%)

Coarse Gaussian SVM (%)

Overall accuracy

97.8

97.2

83.4

98.9

98.6

83.7

Overall error

2.2

2.8

16.6

1.1

1.4

16.3

We passed the extracted 33 features from the VeReMi Extension dataset to the Genetic algorithm for feature reduction phase. The genetic algorithm reduced the feature space to 14 features thus reducing the dimensionality of the dataset to 6000 × 14. Comparing the performance of the two methods, the ANN fitness function based genetic algorithm optimized SVM has shown improved results. The comparison has been summarized in Table 1. The ANN fitness function based genetic algorithm optimized SVM has shown the best results with an overall accuracy of 98.9 with Fine Gaussian kernel function. The confusion matrix and ROC curve is shown in Fig. 6.

7 Conclusion and Future Work As a result of this research, we were able to develop a hybrid intrusion detection system (IDS) that identifies the behavior of vehicular nodes in VANETs by combining SVM with Genetic Algorithm and ANN. The proposed intrusion detection system utilizes the hybrid of ANN fitness function based Genetic Algorithm and Support Vector Machines to detect any malicious vehicle in the network. The IDS is used to assess the behavior of each vehicle in order to determine whether a vehicle is causing a denial-of-service attack or it is just a normal vehicle. In case where the vehicle is attempting to block accessing of the network’s resources and make them inaccessible to other vehicles classified as a denial of service (DoS) attacker vehicle. As future work, we intend to make an effort to enhance the model in order to better detect the intrusive behavior.

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Fig. 6 Confusion matrix (above) ROC curve (below)

Acknowledgements This research was partially supported by Gigasheet.co. We thank gigasheet.co for providing us enterprise access to their website for converting json data to csv, that greatly assisted the research.

References 1. Naqvi I, Chaudhary A (2021) Intrusion detection using soft computing techniques in VANETs. In: 2021 9th International conference on reliability, infocom technologies and optimization (trends and future directions) (ICRITO). IEEE, pp 1–4 2. Zanella A, Bui N, Castellani N, Vangelista L, Zorzi M (2014) Internet of things & smart cities. IEEE Internet Things J 3. Ali Alheeti KM, McDonald-Maier K (2018) Intelligent intrusion detection in external communication systems for autonomous vehicles. Syst Sci Control Eng 6(1):48–56 4. Aneja MJS, Bhatia T, Sharma G, Shrivastava G (2018) Artificial intelligence-based intrusion detection system to detect flooding attack in VANETs. In: Handbook of research on network forensics and analysis techniques. IGI Global, pp 87–100

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5. Sharma S, Kaul A (2018) Hybrid fuzzy multicriteria decision making based multi cluster head dolphin swarm optimized IDS for VANET. Veh Commun 12:23–38 6. Adhikary K, Bhushan S, Kumar S, Dutta K (2020) Hybrid algorithm to detect DDoS attacks in VANETs. Wirel Pers Commun 114(4):3613–3634 7. Gao Y, Wu H, Song B, Jin Y, Luo X, Zeng X (2019) A distributed network intrusion detection system for distributed denial of service attacks in vehicular ad hoc network. IEEE Access 7:154560–154571 8. Kang MJ, Kang JW (2016) Intrusion detection system using deep neural network for in-vehicle network security. PLoS ONE 11(6):e0155781 9. Naqvi I, Chaudhary A, Kumar A (2022) A systematic review of the intrusion detection techniques in VANETS. TEM J 11(2):900–907 10. Raghuwanshi V, Jain S (2015) Denial of service attack in VANET: a survey. Int J Eng Trends Technol (IJETT) 28(1):15–20 11. Kamel J, Wolf M, Van Der Hei RW, Kaiser A, Urien P, Kargl F (2020) VeReMi extension: a dataset for comparable evaluation of misbehavior detection in VANETs. In: ICC 2020–2020 IEEE international conference on communications (ICC). IEEE, pp 1–6 12. Choudhury A (2019) Curse of dimensionality and what beginners should do to overcome it. Analytics India Magazine, May 22, 2019. [Online]. https://analyticsindiamag.com/curse-ofdimensionality-and-what-beginners-should-do-to-overcome-it/. Last accessed 03 Sept 2022 13. Hu Y, Wang B, Sun Y, An J, Wang Z (2020) Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home. Int J Distrib Sens Netw 16(11):1550147720971513 14. Halim Z, Yousaf MN, Waqas M, Sulaiman M, Abbas G, Hussain M, Hanif M et al (2021) An effective genetic algorithm-based feature selection method for intrusion detection systems. Comput Secur 110:102448 15. Savas C, Dovis F (2019) The impact of different kernel functions on the performance of scintillation detection based on support vector machines. Sensors 19(23):5219 16. Kumar A, Sinha N, Bhardwaj A (2020) A novel fitness function in genetic programming for medical data classification. J Biomed Inform 112:103623

Digital and IoT Forensic: Recent Trends, Methods and Challenges Neha, Pooja Gupta, Ihtiram Raza Khan , and Mehtab Alam

Abstract Digitalization contains a diverse set of information that plays a vital role in investigations, from a forensic stand point. Forensic investigations in the IoT/digital paradigms will need to develop and mature in order to meet the characteristics of IoT. In this paper the authors have investigated digital forensics and have addressed the method of processing the sources of evidences in the digital/IoT ecosystem. The authors have further outlined the guidelines, procedures, current trends and a few criteria and roadblocks in the process of digital investigation. This study lays open ways for investigating strategies and methodologies which support the implementation of digital forensics in the dynamic digital systems. This provides a deep and detailed comprehensive understanding of digital forensics and IoT forensics. Further, current problems and issues are highlighted which will inspire and motivate researchers for further research. Keywords Digital forensic · IoT forensic · Social media forensics · Cloud forensic · Anti-forensic

1 Introduction The unprecedented advancement of technology is leading the entire world towards digitization and digital domains. This is leading the concept of the Internet of Things (IoT) in becoming a regular and important part of the lives of people around the world. People are becoming more and more dependent on smart devices, smart automation and other such techniques [1]. These comprehensive digital evidence libraries in the IoT paradigm can reveal a lot about our routine activities which is immensely important to digital forensics. In contrast, the proportion of disputes involving smart products or services has also surged. As a result, there is an acute need for Digital/IoT forensics research to determine the complete action line of incidents. The growing proliferation of Neha · P. Gupta · I. R. Khan (B) · M. Alam Jamia Hamdard, New Delhi 110062, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_6

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connected devices widens the types of digital evidence from computers and laptops to a spectrum of connected devices such as wearables etc. and cloud-based services, posing a number of issues for forensic experts. There is still a compelling need to adapt traditional techniques, practices, and regulations to cope with the key attributes of IoT, even though traditional forensic methodologies and technologies continue to be relevant in various phases of forensics in the IoT domain [2]. This paper covers diversity in topics and offers a comprehensive overview of Digital forensic key guidelines for investigators, the investigation process, recent trends in digital forensics and significant barriers. Section 1 provides a short introduction to digital and IoT forensics. Section 2 briefly discusses the term forensics. Section 3 elaborates on Digital forensics in depth. Section 3 describes IoT forensics in great detail. We have concluded the paper in Sect. 4. References are listed at the end.

2 Forensics The science that deals with forensics is known as forensic science, also known as simply, forensics. It incorporates the use of scientific techniques which are then helpful in answering questions that arise in a legal or criminal investigation or a court case. Earlier, forensics was only related to physical analysis such as the analysis of DNA, fingerprints, blood strains, serology, toxicology, hair, etc. In recent times, with digital advancement, digital forensics has risen rapidly [3]. And, with the current reach and penetration of IoT in our lives, IoT forensics is also rising at an unprecedented rate. The primary activity performed in forensics investigation is the gathering of evidence from various sources and then performing analysis on the same [4]. The process of gathering the evidence and its handling requires precise and procedural steps that have to ensure the following two steps: ● The evidence needs to be acquired legally, ie. With the permission of the court or accompanied by the directive of an authorized organization, person, or institution; ● A pre-defined chain of custody needs to be followed, which makes sure that the gathered evidence is in an untampered and unaltered form, right from the time it was collected until the time it is presented.

3 Digital Forensics The science of exhibiting, recording, assessing, preserving, and detecting data and evidence from digital or electronic devices while respecting user privacy is known as digital forensics. In addition, it recreates and describes the pattern of events using scientific methodology. Digital forensics attempts to employ such unlawful artifacts as testimony by analyzing, examining, and documenting these

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sequences. NIST describes digital forensics as the scientific process of identifying, extracting, inspecting, and interpreting data while maintaining data integrity [5]. It entails conserving, retrieving, verifying, detecting, interpreting, documenting, and disseminating crime scene data. Digital evidence recovered via digital forensics must be handled very carefully, therefore, a forensic investigator must adhere to the following guidelines: i. Minimize the amount of original evidence examination. Instead, look at the duplicate evidence. ii. Adhere to the norms of evidence and avoid tampering with the data. iii. Create a chain of custody for all evidence and treat it carefully. iv. Never go above the FI’s scope of expertise. v. Note any changes in the findings.

3.1 Steps Involved in Digital Forensics Investigation The following stages are included in the digital forensic investigation process [5, 6] as depicted in Fig. 1: Assessment Forensic investigators should carefully dissect digital evidence in light of the case’s specifications to determine which course of action to take. Acquisition Digital evidence is subject to tampering, deletion, or permanent elimination due to its nature and as a result of inappropriate management or inspection. An examination should ideally be conducted using a copy of the original proof. This acceptable form of proof should be compiled in a way that ensures and sustains its integrity. Examination The purpose of the examination is to collect and evaluate digital evidence. In this context, the term “extraction” refers to data restoration from its medium.

Fig. 1 Steps involved in digital forensics investigation

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Analysis This basically involves interpreting the information that has been retrieved and structuring it in a logical and useful form. Documenting and Reporting It is essential to record all activities and observations during the forensic analysis of the evidence in order to keep track of them. The activity will be completed by drafting a report detailing the findings.

3.2 Current Digital Forensics Trends Various processes, techniques, and methods with forensic backgrounds have crept into this domain. Digital forensics has investigated cutting-edge trends and techniques to collect and analyze digital evidence from various sources. Popular digital forensic trends are listed in this section [5, 7, 8]. Cloud Forensics Cloud computing provides large resource coordination, a practical approach, scalability, and broad storage access. There are hybrid, private, and public cloud computing models. Digital forensics has a potential need in a cloud-based system which is referred to as cloud forensics. The standard features of a cloud computing paradigm, which includes levels of cloud computing, data duplication and jurisdiction, and timesharing architectures add several layers of complexity to cloud forensics. This field uses scientific methods, tried-and-true techniques, and technological procedures to execute events in the cloud environments through coverage, investigation, retention, acquisition, and recognition of digital information [6]. Legal, organizational, and technical categories can be employed to classify cloud forensics [7]. To ensure that digital forensics techniques do not infringe on laws and regulations, the legal component is responsible for establishing agreements and guidelines. The organizational component, on the other hand, includes organizational aspects of digital forensics [8]. The techniques and equipment required to carry out a forensic examination in a cloud computing environment are covered by the technical dimension, which is the final subdimension. It is made evident that cloud forensics is growing to become among the top trends in the field of digital forensics [9]. Social Media Forensics The development of Web 2.0 and Industry 4.0 technologies has significantly aided social media platform adoption, making it a prime source of social interaction. Through these websites, users frequently submit their information, sign-in accounts, and engage in social interactions [6]. The most convenient place to find data on potential offenses, perpetrators, and evidence is on social media platforms, which are also the best tools for profiling.

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Investigation teams have access to a current and comprehensive subset of data sources, such as demographic position, photos, contact information, location, and texts and emails, by merging social media with digital forensics. This network data may assist with digital forensics investigations when paired with the metadata. Additionally, the metadata can be employed to verify information from social media platforms [10]. It could be asserted that social media forensics is an emerging trend in the field of digital forensics because of its efficacy in establishing adequate digital evidence. These online domains now have the capacity to log digital forensic evidence or traces that can be a significant tool in an examination owing to the emergence of social media apps across a wide variety of platforms [11]. Since these traces are important elements of evidence, social media forensics is among the most well-known advancements in digital forensics. Figure 2 demonstrates social media forensics in investigation. Reverse search inclusion, spatial positioning analysis, information (metadata) visualization and extraction are the three aspects provided by social media forensics. The first feature of Google Image Search is that it displays the results in a new tab in the computer browser. Second, it includes six distinct tampering localization/ clustering maps that were produced using forensic analytics and are intended to explore numerous traces of social media manipulation. Thirdly, it reveals the potentially integrated thumbnails and completely enables metadata listing [12]. These features enable forensic professionals to go into greater detail and retrieve relevant evidence. IoT Forensics IoT Forensics is discussed in great detail in the upcoming section.

Fig. 2 Social media forensics in investigating a crime

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3.3 Classification of Digital Forensics Concerns The progress of technological breakthroughs in this century has made a significant contribution to criminal prosecution. Technology has enhanced forensic science, particularly digital forensic examination. The effective implementation of digital forensics examination techniques nevertheless faces some impediments. Three main categories as Technical, Legal and Resource are used to classify digital forensic concerns as [13–15]. Technical Digital forensic investigators employ forensic methods to capture empirical evidence against offenders, and such toolkits are used by criminals to conceal, modify, or eliminate the traces of his\her offenses; in digital forensics, this practice is known as anti-forensics. It is recognized as a significant challenge in this field. Some main technical hurdles in digital forensics are encryption, data masking, cloud computing, time to decrypt data, skill shortage, and steganalysis. Legal The integrity of electronic evidence is frequently disputed in court. Privacy concerns, evidence acceptance in Courts, electronic evidence preservation, the capability to capture digital evidence, and Lack of standard policies and norms are some prime constraints in digital forensics. In addition to this, The Indian Evidence Act of 1872 has also limitations. It has a restricted approach, is unable to adapt and change, and does not adequately address the E-evidence, which is more prone to manipulation, distortion, duplication, etc. The Act does not refer to how electronic evidence is procured; instead, it simply addresses how it must be presented in court together with a certificate in accordance with Section 65B, paragraph 4. This indicates that regardless of the process adopted, it must be verified with the use of a certificate [16]. Resource Forensic professionals employ a variety of technologies to verify the validity of the data in order to make the investigation process efficient and effective. Barriers are related to resources include volume, replication, and technological development. Electronic document authenticity, privacy, and accessibility can all easily compromise. Deployment of area network protocols enables data to stream beyond physical boundaries. The volume of data has increased as a result of enhanced communication and accessibility to electronic documents, making it more difficult to identify unique and significant content.

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4 IoT Forensic IoT refers to the concept where devices are connected with each other and also with the internet based on various communication protocols. This technology offers numerous opportunities and with the advent of the idea of smart living, it has become inevitable. Every aspect of our lives ranging from smart homes, and healthcare to manufacturing involves IoT systems. Hyper-connectivity is the future. All these systems work on the basis of data collection and a significant amount of this data is of evidentiary value. The data collected is concerned with location, health, routine, financial data etc. aimed at learning and predicting the behavior of people. It is stored electronically and can be accessed with the help of forensic tools and technology [17]. So, the sensitive and valuable information of an individual or institution is at more risk of getting exposed to unauthorized entities and increases the possibility of cyber-crimes. The IoT equipment is utilizing commercial software and firmware and can also be controlled remotely. Cyber attackers can exploit the vulnerabilities that might be present in the software/firmware of IoT devices [18]. Not only public networks but private devices such as smartphones, and cars can be attacked.

4.1 Steps Involved in IoT Forensics IoT systems are a group of various devices, communication protocols, standards etc. which makes it quite complex. The heterogenous devices and the technologies, such as, cloud computing and RFID, used in the IoT system, lead to a large number of challenges for data protection [19]. New threats and complications are arising every day in the traditional forensic techniques. Figure 3 illustrated the steps involved in IoT forensics.

Fig. 3 Steps involved in IoT forensic

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Fig. 4 IoT security challenges

4.2 IoT Security Challenges IoT systems to a great extent are utilizing cloud platforms for storing and processing data. Due to various service level agreements, access to this data by forensic investigators is restricted. The lack of complete data poses difficulties in the investigation process. There are a number of security challenges and limitations in IoT [20]. They are the roadblocks to the mass adoption of this technology. Some of these challenges and limitations are discussed below and depicted in Fig. 4. Privacy The omnipresence of the IoT environment has resulted in a significant issue on the security front i.e. user privacy and data protection. IoT devices collect valuable data about people and organizations, many times even without their knowledge. This data is stored, processed and transmitted via the internet which poses a threat to the privacy of an individual [21]. Devices themselves can be attacked and controlled comprising the data stored or forwarded from it. The compromised device can further be used to control other devices in the network [22]. Apps for these devices can also be the target of privacy attacks. Attackers can bypass user permissions and take control of devices used to run these apps which can then be used to spy on an individual’s location, conversations etc. [23]. Authentication In an IoT environment, there is a need for authentication between the devices and users. This makes identity authentication a crucial aspect of IoT systems [18]. Efficient key management is also a challenge as it can cause overhead at the IoT nodes [24]. Moreover, methods are needed for the validation of cryptographic keys and securing the integrity of key transfer [25].

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Heterogeneity Multiple types of sensors and actuators are connected in IoT systems. These devices are meant to perform different functions and use different technical interfaces, standards, configurations, release versions etc. This heterogeneity creates the requirement for a protocol compatible with all different devices [26]. One more challenge before the IoT environment is its dynamicity. A device connected to a group of devices at a certain time can be connected to an entirely different group of devices at a later time. Hence, the security protocols and key management must be able to tackle this to make sure the privacy and security of the system [27]. Policies Since IoT systems are dynamic and heterogeneous in nature, the security policies that are in place currently might not be adequate to handle the new challenges arising every day. These new policies need to be developed for effective management and protection of data stored or transmitted. A mechanism to enforce these policies is equally important to make sure every component is following the standards. A Service Level Agreement (SLA) needs to be developed to build the trust of people in the IoT environment which is essential for its scalability [18].

5 Conclusion The literature describes the forensic system, which assists investigators in the process of acquiring and dealing with the information. The stages for forensic acquisition have been highlighted, as well as potential problems in implementation. This study provides a summary of the digital forensics approach and should contribute to the fabrication of significant evidence in offenses.

References 1. Alam M, Khan IR, Alam MA, Siddiqui F, Safdar T (2022) IoT framework for healthcare: a review. In: IEEE world conference on applied intelligence and computing (AIC), Sonbhadra, India 2. Alam M, Khan IR, Tanweer S (2020) IOT in smart cities: a survey. Juni Khyat 10(5):89–101 3. Herrera LA (2020) Challenges of acquiring mobile devices while minimizing the loss of usable forensics data. In: 8th international symposium on digital forensics and security (ISDFS), Beirut, Lebanon 4. Cedillo P, Camacho J, Campos K, Bermeo A (2019) A forensics activity logger to extract user activity from mobile devices. In: Sixth international conference on eDemocracy & eGovernment (ICEDEG), Quito, Ecuador 5. Kent K, Chevalier S, Grance T, Dang H (2006) Guide to integrating forensics Techniques into incident response. Nis special Publication, 80–86

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6. Wazid M, Katal A, Goudar RH, Rao S (2013) Hacktivism trends, digital forensic tools and challenges: a survey. In: IEEE conference on information & communication technologies, April 2013 7. Raun K, Carthy J, Kechadi T, Baggili I (2013) Cloud forensics definitions and critical criteria for cloud forensic capability: an overview of survey results. Digit Investig 10(1):34–43 8. Ruan K, Carthy J, Kechadi T, Crosbie M (2011) Cloud forensics. In: IFIP international conference on digital forensics, Heidelberg, Berlin 9. Alghamdi MI (2021) Digital forensics in cyber security—recent trends, threats, and opportunities. In: Cybersecurity threats with new perspectives, London 10. Sharma BK, Joseph MA, Jacob B, Miranda LCB (2019) Emerging trends in digital forensic and cyber security—an overview. In: Sixth HCT information technology trends (ITT) 11. Rocha AEA (2016) Authorship attribution for social media forensics. IEEE Trans Inf Forensics Secur 12(1):5–33 12. Basumatary B, Kalita HK (2022) Social media forensics—a holistic review. In: 9th international conference on computing for sustainable global development (INDIACom), New Delhi, India 13. Richard III GG, Roussev V (2006) Next-generation digital forensics. Commun ACM 49(2) 14. Rogers M (2003) The role of criminal profiling in computer forensic investigations. Comput Secur 22(4):292–298 15. Fahdi ML, Clarke NL, Furnell SM (2013) Challenges to digital forensics: a survey of researchers & practitioners attitudes and opinions. In: Information security for South Africa, Johannesburg, South Africa 16. I. India (2022) Indian Evidence Act, 1872. https://legislative.gov.in/sites/default/files/A187201.pdf 17. Stoyanova M, Nikoloudakis Y, Panagiotakis S, Pallis E, Markakis EK (2020) A survey on the Internet of Things (IoT) forensics: challenges, approaches, and open issues. IEEE Commun Surv Tutor 22(2):1191–1221 18. Sathwara S, Dutta N, Pricop E (2018) IoT forensic a digital investigation framework for IoT systems. In: 10th international conference on electronics, computers and artificial intelligence (ECAI), Iasi, Romania 19. Janarthanan T, Bagheri M, Zargari S (2021) IoT forensics: an overview of the current issues and challenges. In: Digital forensic investigation of Internet of Things (IoT) devices. Springer, pp 223–254 20. Neha PG, Alam MA (2022) Challenges in the adaptation of IoT technology. In: A fusion of artificial intelligence and Internet of Things for emerging cyber systems, intelligent systems reference library, pp 347–369 21. Lopez J, Rios R, Bao F, Wang G (2017) Evolving privacy: from sensors to the Internet of Things. Futur Gener Comput Syst 75:46–57 22. Tilley A (2015) How hackers could use a nest thermostat as an entry point into your home. Forbes, 6 March 2015. https://www.forbes.com/sites/aarontilley/2015/03/06/nest-thermostathack-home-network/?sh=75794ab83986 23. Winder D (2019) Google confirms android camera security threat: ‘Hundreds of Millions’ of users affected. Forbes, 19 November 2019. https://www.forbes.com/sites/daveywinder/ 2019/11/19/google-confirms-android-camera-security-threat-hundreds-of-millions-of-usersaffected/?sh=70cb110c4f4e 24. Yang Y, Cai H, Wei Z, Lu H, Choo, KK (2019) Towards lightweight anonymous entity authentication for IoT applications. In: Australasian conference on information security and privacy 25. Conti M, Dehghantanha A, Franke K, Watson S (2018) Internet of Things security and forensics: challenges and opportunities. Futur Gener Comput Syst 78(2):544–546 26. Zhao K, Ge L (2013) A Survey on the Internet of Things security. In: Ninth international conference on computational intelligence and security, Emeishan, China 27. Mahmoud R, Yousuf T, Aloul F, Zualkernan I (2015) Internet of things (IoT) security: current status, challenges and prospective measures. In: 10th international conference for internet technology and secured transactions (ICITST), London, UK

Cloud-Based Occlusion Aware Intrusion Detection System Deepak Sharma, Dipanshu Tiwari, Vinayak Singh, Priyank Pandey, and Vishan Kumar Gupta

Abstract This paper proposes a cloud-based occlusion-aware intrusion detection system that detects the faces of intruders and matches them to a database for recognition. A novel face detection or recognition system is proposed that enables robust face detection and recognition even in the case of occlusion, blurred images, face masks, side views, or even partial views of faces. It does so by focusing on all the visible features like eyes, ears, nose, etc., and using those for facial recognition. The proposed recognition system can match the side view of a face with the front view using the learned embeddings. The user gets an email notification as soon as an intruder is detected whose face does not match any person in the database. The entire system is deployed as a service on Google Cloud. Cloud deployment helps in removing any local computation requirement. The model is constantly updated via the Vertex AI pipelines feature of Google Cloud. Keywords Face detection · Face recognition · Single-shot detectors · Occlusion

1 Introduction Extensive research in deep learning and computer vision has led to the development of algorithms and systems that are being deployed the world over for various tasks, ranging from crop monitoring in the agricultural sector to human detection and identification in surveillance applications. All these applications rely on the robustness and efficiency of the core computer vision algorithms working at the backend. Also, with the advent of the latest cloud technologies. Even though the problem of face detection and recognition or object detection, in general, has been researched quite deeply over the last decade, there are still problems with the state-of-the-art methods [1, 2] when tested in the real-world environments. D. Sharma · D. Tiwari · V. Singh Bhai Parmanand DSEU, Shakarpur Campus-2, New Delhi, India P. Pandey · V. K. Gupta (B) Graphic Era Deemed to be University, Dehradun, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_7

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Problems related to fast-moving objects (causing motion blur, occlusion), blurry or noisy images, partial view of the face, or any other object cause problems in their detection and recognition. Also, there remains a trade-off between the computational performance and accuracy of algorithms. The rising need for faster and compute efficient algorithms for face detection for various real-world applications has further created demands for developing algorithms for resource-constrained systems. While the traditional methods [3, 4] making use of hand-crafted features offer high computational speed but are very sensitive to face pose, scale, noise in the image, etc. The use of deep convolutional neural networks (CNN) led to landslide improvements. For tackling the scale issue, early researchers focused a lot on the Cascade of Classifiers Idea and developed models based on Cascade-CNN architectures [5]. Cascaded models worked fine in the case of multi-scale face detection but were slow. The development of several novel architectures for general object detection led to improvements in face detection as well. Models based on Single-shot detection (SSD) [6] were developed that enabled fast and accurate detection among other algorithms like R-CNN [7], feature pyramidal network [8], and more. The introduction of the attention mechanism in face detection [9] has shown better results than the previous methods but does increase the training and inference computational time. Practical applications of deep learning and computer vision make use of various training and deployment methodologies. Cloud-based deployment is extremely popular these days as it nullifies the need for a local computing system and also provides several features that enable the development of training and deployment pipelines. Companies like Netflix, Tesla, and others make use of cloud computing infrastructure for storing data and maintaining pipelines for regular model training and deployment. In this work, a cloud-based intrusion detection system is proposed that works by detecting faces and matching them to a database of images for recognition and notifying the user via email about the intrusion. The face detector proposed in this paper works even in case of high occlusion, blur, or partial view of the face (side view or face mask) and can match that face correctly with the database. This is done by learning facial embeddings that focus on all visible portions of the face like eyes, nose, ears, etc. that could give potential matching candidates. For developing such a system, multiple datasets were used out of which the RMFD dataset provided face data wearing masks, and for other faces without masks, the usual part of the face that mask covers were colored black. By augmenting data in this way, several augmented faces were created for each candidate’s face and the corresponding representations were learned by training an autoencoder. This enabled learning partially visible features for facial recognition. For deploying the system, Google Cloud Services have been used. The proposed pipeline has been described in the next section.

2 Proposed Work A flowchart for the proposed work is shown in Fig. 1.

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Fig. 1 Flowchart of the entire project

There are three main sub-modules in the project as listed below, – Face Detection – Face Recognition – Deployment on Google Cloud.

2.1 Face Detection The basic architecture used for face detection is based on a mobileNet-v2 with a single shot detector (SSD) block. In order to detect partially visible features or learn face-representing features, a multi-scale approach has been taken with the mobileNet encoder where outputs from shallow layers are concatenated at the layers lying in depth. This enables the encoder to learn low-level as well as high-level features of the face. This single shot detector or SSD enables fast face detection thus leading to real-time performance of the algorithm (Fig. 2). The face detection model is trained with a combination of datasets containing images with and without masks. For the images where face masks are not present, a portion of the face that should have been covered with the mask is colored black. This augmentation step helps to learn distinct features that can detect faces that are partially visible. The WIDER Face dataset [10, 11] contains face images without masks, and the RMFD dataset [12] contains face images with masks. The proposed face detector method performs well on both datasets and

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Fig. 2 Face detector with SSD

Fig. 3 Face without mask (left) and with mask (right)

Fig. 4 Mask augmentation

gives better results than the current state-of-the-art algorithms. Figure 3 displays two images, one without a mask (from WIDER set) and one with a mask (from RMFD dataset). Figure 4 shows the augmentation where an artificial mask is put on the face.

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2.2 Face Recognition Face recognition is performed by matching the embeddings of the face image under consideration to a dataset of photos provided by the user. Now, the face currently visible could be in any orientation, which poses difficulties in matching to a database that may or may not contain views of the same face from different angles. Usually, in a database, there is only a frontal view available and the same constraint is presumed here. This raised the need to develop a method that could match various views of the face with each other. Since the manual grouping of similar faces is a tedious process, an unsupervised approach is taken here for determining the similarity or dissimilarity between faces. Figure 5 shows various views of a face obtained from various angles. In order to learn view-independent embeddings of face, a representation learning approach is taken as described in [13]. For each face, different views are taken along with masks and also, and augmentation is done by covering random portions of the faces and they are trained to be detected as similar using a Siamese network with a contrastive loss. For dissimilar pairs, different faces are put together (Fig. 6). The architecture of the siamese network is the same as described in [13]. Augmenting faces with different views, masks, partial visibility, etc. enables the encoder

Fig. 5 Views of face from different angles

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Fig. 6 Views of a face from different angles

Fig. 7 Vertex AI pipeline

to learn representations that help in detecting similarity. Learning such representations leads to matching side/partial views of faces with each other and thus better results than the current state-of-the-art models (Fig. 7).

2.3 Deployment on Google Cloud Deep learning inference may require a decent amount of computational power which may not always be available locally. Even on low compute platforms, it is a difficult task to install and maintain deep learning libraries and modules. Moreover, the deployed models cannot be controlled centrally. This is where the power of cloud

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Fig. 8 Face recognition results graph

computing can be leveraged. The face detection and recognition model described above is extremely lightweight but still requires libraries like Tensorflow to execute. Also, cloud computing platforms offer services that not only help in training a model but enable us to design the entire cycles for training, maintenance, and deployment of machine learning models. Google Cloud Platform is one of the most powerful and easy-to-understand cloud computing services. The Vertex AI Platform on the google cloud platform helps in the easy development of model training and deployment pipelines. We create a custom training job as our model is designed by us instead of using AutoML. The custom training job takes data from google cloud storage (a data storage platform offered by google cloud platform). Figure 8 shows the pipeline consisting of data feeding, model training, and deployment via cloud run.

3 Experimental Results We performed experiments on online available datasets for face detection and recognition and compared them with the state-of-the-art models on metrics Average Precision . A P and computational time. We also describe the training details of the models. Calculation of Average Precision is done using other metrics such as the Intersection over Union (.IoU), and Confusion Matrix. A confusion matrix is constructed using True Positives (.TP), False Positives (FP), True Negatives (TN), and False Negatives (.FN). These metrics are then used to calculate Precision (. P) and Recall (. R). .IoU is used to quantify how close two bounding boxes for an object are (the predicted bounding box and the ground truth bounding box). The range for .IoU is [0,1]. If there is a complete overlap between two bounding boxes, then the value of .IoU is 1 and when there is no overlap, then it is 0. .IoU is calculated by taking the ratio between the area of the intersection and the area of the union of the bounding boxes. For a prediction to be correct, the .IoU value has to be above a pre-defined threshold.

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Now, .IoU is one metric. Using this and the class labels of the ground truth and predicted boxes for object detection, another three metrics are calculated which are stated below – True Positive: The bounding box predicted by the model (positive) actually exists at that location as per ground truth (true) [14]. – False Positive: The bounding box predicted by the model (positive) does not exist at that location as per ground truth (false [15]. – False Negative: The model failed to predict a bounding box (negative) when it was actually there in the ground truth (false) [16]. – True Negative: The model did not predict a bounding box (negative) when it was not actually there in the ground truth (true) [17]. Based on the TP, FP, and FN, for each labeled class, we calculate two parameters: precision and recall. – Precision: Precision tells us how much of the predicted results are actually true, i.e., out of all positives predicted, how many are true positives? The goal of precision is to minimize false positives. – Recall: Recall tells us how much of the positive area from the ground truth is covered in prediction. A model with high recall tends to include the entire true positive area even if it has to include some false positives. The goal of recall is to minimize false negatives. Average Precision (. A P) is calculated using Precision-Recall (PR) curve. A PR curve is calculated using precision and recall values on the x and y axes. AP summarizes the PR curve to one scalar value by calculating the area under it. For AP to be high, both precision and recall values have to be high thus it gives a good balance between the two. The range of AP is [0,1]. The proposed face detection and recognition models were trained on an A100 GPU offered on the Google Cloud Platform. The model was trained for 120 epochs at a learning rate of 0.001 and a decay rate of 0.5. The batch size was taken to be 32 and early stopping was used to prevent overfitting of the model. For inference, an i7–8th Generation CPU was used (container specifications). Since we wanted to use CPU for inference to lower the cost of the end system, we did not use any GPU. Table 1 summarizes the results of face detection from our comparison and also the comparison with other models on the WIDER dataset. We compare our model with Poly-NL, Retina Face, and AInnoFace. Similarly, we show our results in Table 2 on masked faces, partially visible faces detection (using the RMFD dataset and our custom additions). Now we compare the results of face recognition on our challenging data consisting of augmented masked faces, side views, partial views, etc. Table 3 shows the results of our face recognition with other state-of-the-art methods. As can be seen, our method outperforms the other methods in case of added difficulties like partial views, masked faces, etc.

Cloud-Based Occlusion Aware Intrusion Detection System Table 1 Face detection results on faces without masks AP Model Our method Poly-NL RetinaFace AInnoFace

0.9548 0.92 0.914 0.912

Table 2 Face detection results on faces with masks AP Model Our method Poly-NL RetinaFace AInnoFace

0.912 0.842 0.804 0.794

Table 3 Face recognition results on faces with masks Model Accuracy Our method Prodpoly DiscFace CircleLoss

0.9881 0.95833 0.9125 0.8917

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Inference FPS 27.25 14.12 18.25 22.5

Inference FPS 27.25 14.12 18.25 22.5

Inference FPS 31.1 18.485 15.5 21

4 Conclusion The proposed face detection and recognition models proposed in this paper outperformed all the state-of-the-art methods in the case of challenging images like faces with masks, partial view matching, etc. This setup is ideal for intruder detection as intruders usually have face covers that prevent easy identification. The face detector and recognizer proposed in this paper can be used in such cases and greatly help in surveillance applications.

References 1. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 779–788 2. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement 3. Cuimei L, Zhiliang Q, Nan J, Jianhua W (2017) Human face detection algorithm via Haar cascade classifier combined with three additional classifiers. In: 2017 13th IEEE international conference on electronic measurement & instruments (ICEMI), pp 483–487

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Comparative Analysis of Web Application Based Encryption Methods Yuvraj Singh, Somendra Singh, Shreya Kandpal, Chandradeep Bhatt, Shiv Ashish dhondiyal, and Sunny Prakash

Abstract The login mechanism of the web base application currently uses the MD5 hash method used for the encryption of the password. The current state has a weakness of Collision Attack, which means the same hash value will be generated for two or more different inputs. If these values are somehow known, then it can be a threat to the user’s privacy, data and even steal of user’s access. In this situation, as a remedy, we are upgrading our encryption method to the SHA512 method. The collection of data is done by using sources like articles, research papers, journals, patients, magazines, and other online and offline sources. The research is also further divided into parts namely analysis, system vulnerability, and remedy. The report must contain a graphic, pictorial representation by using flowcharts and graphs to make concepts and theory clear about the working mechanism. For testing the finalized product, we conduct a test called UAT. In this test, users come forward to test the application before launching it into the market. In our case, UAT results show that 86% of voters strongly agree with replacing MD5 with SHA51. So, the implementation of patch security at the time of the login process is going to be implemented by SHA512 for further. Keywords Encryption · Hashing · MD5 hash method · And SHA-512

Y. Singh · S. Singh · S. Kandpal · C. Bhatt (B) Graphic Era Hill University, Dehradun, Uttarakhand, India e-mail: [email protected] S. A. dhondiyal Graphic Era Deemed to be University, Dehradun, Uttarakhand, India e-mail: [email protected] S. Prakash GL Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_8

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1 Introduction Hashing is a process of converting a given data like integers, characters or strings into another value that can’t be directly understood. Commonly this binary data is of fixed length and comparatively shorter lengths depending on the method used [1]. Common and popular uses of hashing are password verification, Message Digest, Data Structure (Concepts), data encryption, compressing of data, compiler operation, and linking of files, board games, and graphics [2]. Especially in today’s cyber world, the issue of breaking up of security mechanisms created for maintaining the security and privacy of users and maintaining access control is so common by intruders who try to gain access illegally [3]. If there is any Vulnerability present on our website, it will work as an open gate for intruders to attack us. The attack could result in even welcoming intruders to our server [4]. The simplest way for doing so is authentication. It simply means that the user is the one he or she claims to be. This check’s function is one of the most necessary elements in computer system security. This makes us find the difference between genuine users, customers, and an intruder with the wrong intentions. Authentication of someone is a must at the login time itself to be in the safe zone. If it is not, the owner organization may face losing and stealing data of its customer and employees. The organization may also face the loss of its valuable assets during the attack. There are many new techniques created for improving the security of data and information. The most common of them is the cryptographic encryption of data [5]. In the basic login process, the encryption of special passwords is a must to let users work in a secure manner and safe environment. The sample web-based application uses TheMD5(Message Digest 5) technique, so we need to shift to a more reliable method [6]. The SHA method has its different variants based on block size, for usage, the data is arranged in blocks of a particular size called blocks. Whenever we apply the secure hashing technique, the length of input string or value could be manipulated, and the resulting output of the MD will also have a different length depending on the method we used. The size of the Message Digest with the different methods is given in Table 1. SHA 1 has an input capacity of (264 –1) bits message size, for hashing it a have 160 bits and for evaluation, it has 2 to power 80 hash power. In the year 2005, an attack was published by “Rijmen” and “Oswal” to reduce the version of SHA1 by using it with 53 rounds and 80 rounds. Then they found collision was there with complexity with about 280 operations [7]. Currently, SHA256 and 384 are used very Table 1 Comparison of each SHA method variation Method

Length of Message Block-size (in bits) Word-size (in bits) Message digest size (in bits) (in bits)

SHA-1

Less than 264

SHA-256 Less than

264

512

32

160

512

32

256

SHA-384 Less than 2128

1024

64

384

SHA-512 Less than 2128

1024

64

512

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less as their security is very high and protected these for it took a very high amount of time for hashing [8]. The SHA 1 is further improved to SHA512 by including improvements of MD4.” MegahMulya” said [9], “the consistency of the method SHA-512 is accomplished by the capability to produce a long 512-bit hash values. No hash function can produce longer hash value than SHA-512. This 512-bit long hash value of SHA-512 makes it more secure against brute-force attack than other hashing methods. Therefore, the SHA-512 is evaluated as fast, strong, and powerful hashing methods in today’s working world.” We have various challenges in this field such as security, authentication, confidentiality, and integrity [10]. The next section of the paper consists of the basic theories and methodologies related to encryption techniques.

2 Basic Theory and Methodologies 2.1 Login System Login in the system is a basic process to authenticate the user that he or she is the one only he or she is claiming to be. For, this we ask for the password associated with the unique id in progress to use for login. Password must be big enough for several characters and should also be containing characters of different types like letters of the upper and lower case, numeric value, and special symbols. The user’s password is already preset in the server of the company. Sending of complete password for checking is not suggested by the prospect of sending a huge size of data and not also stealing passwords in between by packet sniffing tools. So, we use first create a hash value out of the password on the user side only then send the hash value only to the server, so there the hash value can compare with the hash value of a pre-registered password, which is beneficial or both safely and convince of password transferring.

2.2 Encryption Method Encryption is a process of jumbling up data in a predefined order so that only authorized parties with the key can understand the information. In technical terms, it is the process of converting data from human understanding language to code language which cannot be read by humans directly, also known as cipher-text [11]. This cipher text does not make any sense to human view. It will just seem to be a collection of strange characters and symbols. The reverse of encryption is decryption. The rearrangement of the scrambled data back into its original text is called decryption. For doing the decryption, you required 2 things first the knowledge of the method used, and second, the value of the key used. A Key is an array of binary digits of data that

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Table 2 Comparison of multiple hash function methods Method

Message digest size (int Bits)

Message block size

Occurrence of collision

MD-5

128-bit

27

occurs

MD-4

128-bit

29

Almost

MD-5

128-bit

29

Occurs

RMD

128-bit

29

Occurs Not occurs

RMD-128/256

128-bit/256-bit

29

RMD-160/320

160-bit/320-bit

29

Not occurs

SHA-0

160-bit

29

Occurs Disability occurs

SHA-1

160-bit

29

SHA-256/224

256-bit/224-bit

29

Not occurs

SHA-512/384

512-bit/384-bit

210

Not occurs

512-bit

29

Not occurs

WHIRpool-0

Note* RMD RIPE Message Digest

is used for making iterations in the binary stored form of data in processes through a cryptographic method, if we get a key we can easily encode and decode the data. It is a necessary element for both encryption and decryption. In most cases, the key is a random string of bits created by some function for a particular encryption only. Key is creating method is designed in such a way that the keys It generates will be unique and unpredictable each time. As longer the key generated is, the harder it is to predict the actual key will be, as adding one bit to the key will double the number of all possible keys. There are many different encryptions available today with different working mechanisms and features like block size, the size of a message digest, and collision probability [12]. A list of commonly used mechanisms (Table 2).

2.3 Types of Encryptions Symmetric Encryption and Decryption. If one common key is used for encryption process and decryption as well, it is called symmetric encryption and decryption. It is also known as secret key encryption as the only key is used so needs to be made hidden from the public. If you encrypt a file using one key and decryption is possible using that value only as a key, which means you are using symmetric encryption [13]. In symmetric encryption, a single key is used for both encrypting and decrypting process. If you encrypt a zip file, then decrypting is possible with that same key only, you are using symmetric encryption. Symmetric encryption is also called “secret key” encryption because the key must be kept secret and hidden from third parties. It is the type of encryption in which one or more mathematical operations and a jumble-up of values are done then for the decryption, all the steps are followed in the reverse order, so the reverse of all the processes will take place,

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and hence we can retrieve the plain text back. The knowledge of operations that took place is part of the method and the fixed numbers or constants for case used are directly or indirectly hidden in key parts. Asymmetric Encryption and Decryption. Public-key encryption also knows as asymmetric encryption. In this approach, we have a pair of keys. One is public and the other is public with each user. The generation is done as such encryption with the public key can decrypt with the private key and vice versa. The method is designed such that both the keys will be independent design, which means with one pair of keys and someone’s public key, you cannot find out his private key [14] (Fig. 1). In ideal case parties legitimate and authorized have access to back the cipher text back to the plain text. Encryption does not stop parties over the internet to steal them but puts a kind of lock that stops them from the real data. Due to some technical reasons, generally encryption methods use semi-unique keys or pseudo-random keys generated by the key generation methods and use those keys only for encryption and decryption without sending keys from user to server. In the implementation of this system, the designer must know the high-level concepts of security of the system. This system gives the facility to an unauthorized person to convert back and see the plain text ack, but not to an unauthorized person. In past years, encryption had played role in cryptography becomes a common thing of day-to-day use, and now it has become a common thing of use. With the wide use of encryption, the number of intruders,

Fig. 1 Basic steps of encryption and decryption

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Fig. 2 SHA 512 architecture

hackers and password crackers also increases. For this reason, the encryption method must e a strong and secure one. In the SHA512 hash method, if we see at a basic level, it is nothing else but only the SHA-2 hash method but at a different level with a longer key size and more rounds of iteration. As shown in Fig. 2, SHA512 hash method works with a message digest of size 512 bits and block length of 1024 bits. The working mechanism of SHA512 is that it takes the input of any length as given or size lies in the maximum limit of the method, making the content of a fixed length of 512 bits called a message digest [15].

2.4 Generation of SHA512 Digest Salting. In the first step, we need to add up another number with the binary code of our plain text such that it is congruent with 780 mod 1024, to maintain the length within 1024. Here 1024 comes to context because the block size in SHA512 is 1024 bits. Spouse message length is 32-bit. The complete message of 890 − (32 + 1) = 857 bits. Further, the length of the message will be from 1 to 890. LMR (Long Message Redemption) Addition. In this step, a length of 128 bits is added up to the starting length of the input message. If the length of the message is excited 2128 bits size, then modulo of 2128 with length is taken in to make it within the range, in other words, if we got a message with a length more length 2128(2^11), let’s assume the length of the message to be L bit, for this example, 128 + L modulo

Comparative Analysis of Web Application Based Encryption Methods Table 3 Hex-Notation SHA-512

Buffer

Initial value

A

6a09e667f3bcc904

B

bb67ae8584caa73r

.

…

…

…

G

1f83d9abfb41bd6c

H

5be0cd12247e2179

85

Fig. 3 Architecture of research methodology

1024 will give the length of the message. So, by the end of the second step, we have a message fixed length of 1024. Hash Value Initialization. Talking about the SHA512 method, as shown in Table 3 the hash value H (0) contains 8 words in the range of 64 bits’ hexadecimal values. This part of the paper explains the overall report and the organized way that needs to be used to solve the problem in the research as well. This section will also contain the steps that are required to implement the testing part and analysis of data in research work. Different levels contain reading literature required to understand and analysis of the systematic approach taken in work to identify the present condition, merits & demerits of the current program. This level requires the study of recommended volumes, relative documents, patents, current events, and all the necessary. It may also include some program codes that may use in the research work (Fig. 3). Here are the requirements for the analysis of data and system vulnerabilities that need to be detected, to find the vulnerabilities present and requirements by the site or computer we are working with. The study in this work is focused to deal with the encryption system and its use for the login system of web-based applications where our major concern will be to get merits and demerits present in our current method and then compare it to the new method are replacing with. Here we spouse to analyze the security concern and for this, the method must implement such construction or betterment of description and show the basic introduction to the hash mechanism. At the time login activity is completed. Further detail explain is done of hashing method, and pictorial representation using graphs, charts diagrams so the logic behind the work can be delivered to the audience in a clear and understanding manner. The process of Mitigation is done by applying the most frequent encryption method executing the function, source code is iterated to see and test the results for taking into work. This time, testing is between two different encryption methods MD5 and

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SHA512. A test was conducted by (IJCSDF) 7(4): 373–381 and one of the authentic societies of wireless communication, 2018 ISSN: 2305–001 of pen-testing and UAT (User Acceptance Test). Pen-testing was conducted using the “Brute Force testing method,” ACT was conducted help of questionnaires that users were asked to fill out and on the base of them, improvements will be recommended.

3 Proposed Method 3.1 Vulnerability Analysis Security of Information means making our data secure from all possible types of threats. In other words, we don’t want to leave any kind of vulnerability in our program. This security analysis will be discussing how the login process will take place in our web application at the time of working. This study will give benefit from understanding the current vulnerabilities present in our web-based applications, so vulnerabilities could be removed, and applications could be improved. This study will discuss the analysis of the coding in our web application using SHA512. In the end, we will conclude the result of the study and study could use as data to refer to, in other words, used as alternative data for the management of the login mechanism of our web application. The problem intruder found while analysis of the application is shown in Fig. 4.

Fig. 4 Capturing login credential Web-based application

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3.2 Analysis for Needs and Betterment This study will include all points and perimeters where improvement is required in our web-based application’s login mechanism. By analyzing our system, we get to know the requirements and possible threats in our system, for the final improved result is updated from the previous once the hash model was in use. After getting to know about the current use hash method, in this case, MD5; the current method needs a quick update by a new one, which will be better to trust. The update of the encryption function was completed to the SHA512 encryption function.

3.3 Mitigate and Test This Step of analysis must get a target for connecting its data belonging to the nonidentical type of variables to contain ample data which could explain the complete incident of attack and events that took place under the network of our application [16, 17]. It includes several things like packages transferring reports and reports of network traffic. For sniffing a website server requite some tools also a packet sniffer or “Wireshark” to deal with teal time traffic going over the network, observe the network and generate a copy of the packets traveling over the internet with their details. In this study test was performed using a tool called “Wireshark.” The tool “Wireshark” is a network packet analyzer tool.” Wireshark” is one of the most widely used, preferred, and recommended tools for packet sniffing tool due to its simple-to-understand and userfriendly interface and advanced features. Once the data is captured, then further we can see and study the sniffed data through this method, and we can get what type of encryption method is used. This analysis is to determine what type of hash function is done. This is determined by the Hash Identifier tools. This analysis was performed so that with hash identifier tools we can get an idea of the hash function used or at least the type of hash function used. The Data traffic result captured is shown in Fig. 5. Here the work has used the tool “Wireshark.” “The data captured is showing the user, admin, information of protocols and encrypted password contains cipher-text have hash value is 154e2803428bb34b2a1c48ffadd177b6. By gaining this amount of data, the information requires extra information that is required by hash identifier.” A basic, brief, and clear understanding of the theory process and backhand work are explained in Figs. 6 and 7. Figure 7 shows the flowchart of login process and process 3 that is to change the previously using MD5 method gets replaced with SHA 512 method. Here, first the input of the password took place. Now the password is encrypted by the SHA512 method whose length is very high compared to MD5, it becomes highly better in security, against intruders and other harmful entities which could come by MD5. For making implementation take place, we need to swap the current encryption

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Fig. 5 Sniffing results using the wireshark application [18]

Fig. 6 Shows the MD5 method used in the login method of the system [18]

Fig. 7 Flowchart of login process by using SHA512

method which is MD5 by better than is SHA512 encryption method with a mixture of salts in a secret key. This stage will contain the creation and implementation of a sample patch and use in our login mechanism. Here work goes as pin the processes & data changing on ID & plain password is done earlier than converting into cipher text, this is done to ensure that calling of function is working properly. After this process, we get a chart of the calling mechanism. This can be seen in Fig. 8. As a result of patching, we come to see the replacement of the method gives in better security, and

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Fig. 8 The hash function calling diagram [18]

the process of salting which we use boosts up the protection mechanism of securing the key.

4 Result and Discussion Penetration testing is suggested and preferred to test the security of any website. It includes all sectors like vulnerability presence, attacks possible and this also define how strong level of the hash method is used. For our testing, we are a penetration testing tool name “Haskcat tool server” to get plain text back from the hash value. A brute force attack was conducted for the key where to get a plain text back with MD5 it took 58 s on average whereas with SHA512 it took 68 s. So as a ground test result, we can say clearly that SHA512 is far better than the MD5 mechanism due to the longer key and secure mechanism. The ‘User Acceptance Test (UAN)’ is a text done by the developer the get review of its product by Users like feedback, whether they like it or not, and how much user like or dislike their application. In the UAT results, responses of users for the security testing with as 8% voting for SS (Strongly Agree), 78% voting for S (Agree), 14%voted N (neutral), and 0.0% voting for TS (Less Agree) & STS (Disagree). The results table of the tests of earlier and later the patching process is shown in Table 4.

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Table 4 Comparative analysis table S.No

Comparative analysisparameter

1

The overall The hash value generated by hashing is value very small, so it consumes less time generated the whenever Brute-force attack is applied hash function

Generated a much more hash value, so it consumes a long time when a Brute-force attack is applied

2

Security parameters for Login attribute

Because it makes use of the basic hash function, and it has been proven that it has unsafe vulnerabilities. Therefore, this method does not satisfy security standard

The hash function used has more consistency and security during encryption. This satisfied the entire security standard

3

Password protection level used during the process of login

Poor password protection, it has been proven that this method has unsafe vulnerabilities

It has better password protection because it has been proved that it is far better in terms of security, protection, and reliability

Before patching

After patching

5 Conclusion On the bases of the result we found, studied, tested, and researched we can conclude this thing “login process” in our “web-based application” needs to be updated from the “MD5 method” to the “SHA512 method.” This step in updating of encryption method will be strengthening the security features of password protection at the time of login. The addition to this update will further protect the” user’ data.” This method update primarily focuses on the improvement of “security of password” at the time of login into our “web-based application” by switching to improved, more reliable, and strong method to be defensive against strong attackers and” cracking” will hard. By taking SHA512 into work, we can produce a key of 512 bits which is very hard to crack and ensure “system security” and “data confidentiality” for us. For “Penetration Testing” against” Brute Force attacks” we use “Hash-cat” tool where “The SHA512 method” proved to be better than the previously used” MD5 method” due to its longer key size, it took more time to predict the correct key. In the UAT test also with a clear majority of 86% of Users who voted as agreed for the update, the adaption of the patch to our “web-based application” at the “login point” was required.

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References 1. Kurniawan E, Riadi I (2018) Security level analysis of academic information systems based on standard ISO 27002:2003 using SSE-CMM 16(1):139–147 2. Riadi I, Aristianto EI, Dahlan A (2016) An analysis of vulnerability web against attack unrestricted image file upload. Comput Eng Appl 5(1):19–28 3. Irfan P, Prayudi Y, Riadi I (2015) Image encryption using combination of chaotic system and rivers shamir adleman (RSA). Int J Comput Appl 123(6):11–16 4. MH W (2009) Development of hash function encryption on SHA (Secure Hash Method). J IlmuComput dan Teknol Inf 3(2):1–7 5. Kumar S, Karnani G, Gaur MS, Mishra A (2021) Cloud Security using hybrid cryptography algorithms. In: 2021 2nd international conference on intelligent engineering and management (ICIEM), pp 599–604. https://doi.org/10.1109/ICIEM51511.2021.9445377 6. Hermaduanti N, Riadi I (2016) Automation framework for rogue access point mitigation in IEEE 802.1X-based WLAN. J Theor Appl Inf Technol 93(2):287–296 7. Kristanto A (2003) Data security on computer networks. PenerbitGava Media, Yogyakarta 8. SSL Information (2018) Difference between hashing and encryption [Online]. https://www.ssl 2buy.com/wiki/differencebetween-hashing-and-encryption 9. Angga C (2011) Analysis of how diverse works hash functions exist, pp 1–6 10. Seth B, Dalal S, Jaglan V, Le DN, Mohan S, Srivastava G (2022) Integrating encryption techniques for secure data storage in the cloud. Trans Emerg Telecommun Technol 33(4):e4108 11. Rosmiati YP, Riadi I (2016) A Maturity Level Framework for Measurement of Information Security Performance. Int J Comput Appl 141(8):975–8887 12. Devi A, Sharma A, Rangra A (2015) A review on DES, AES and blowfish for image encryption & decryption. Int J Comput Sci Inf Technol 6(3):3034–3036 13. Bokhari MU, Shallal QM (2016) A review on symmetric key encryption techniques in cryptography. Int J Comput Appl 147(10) 14. Kumar S, Gaur MS, Sagar Sharma P, Munjal D (2021) A novel approach of symmetric key cryptography. In: 2021 2nd international conference on intelligent engineering and management (ICIEM), pp 593–598. https://doi.org/10.1109/ICIEM51511.2021.9445343 15. Cheng H, Dinu D, Großschädl J (2018) Efficient implementation of the SHA-512 hash function for 8-bit AVR microcontrollers. In: International conference on security for information technology and communications. Springer, Cham, pp 273–287 16. Singh P, Bordoloi D, Tripathi V (2021) Image decryption and encryption using a cellular automaton with just two dimensions. Webology 18(5):3185–3190 17. Zhou S, He P, Kasabov N (2020) A dynamic DNA color image encryption method based on SHA-512. Entropy 22(10):1091 18. Sumagita M, Riadi I, Sh JPDS, Warungboto U (2018) Analysis of secure hash algorithm (SHA) 512 for encryption process on web-based application. Int J Cyber-Secur Digit Forensics (IJCSDF) 7(4):373–381

Linking of Ontologies for Composition of Semantic Web Services Using Knowledge Graph Pooja Thapar and Lalit Sen Sharma

Abstract To enhance Web Services interoperability for better composition, Semantic Web Services (SWS) architecture offers an opportunity to add higher semantic levels in the existing frameworks using ontologies. Semantically described services will provide better service discovery and allow easier composition and interoperation. Knowledge graphs (KG) use ontologies as the core model to represent formal semantics in knowledge representations and therefore can be effectively utilized in the composition of SWS. Reasoning over knowledge graphs is the new area of research to infer consistent and reliable information from existing data. In this paper, we have proposed and implemented a framework for reasoning over KG using subclass inference that has achieved an average precision of 79.87% and an average query response time of 2.02 s for 37 user queries from 9 domains in the OWL-S dataset. Keywords Semantic web services · Composition · Ontologies · Knowledge graph · Reasoning

1 Introduction Metadata is “data about data” which is machine understandable information about web resources. Different types of metadata provide technical, descriptive, administrative, and semantic characteristics of web resources and thus allow their digital identification, resource discovery by relevant criteria and also facilitate interoperability using metadata schemas [1, 2]. Semantic Web or Web 3.0 has enabled information exchange about web resources regardless of applications, community, and enterprise boundaries. It defines a named relationship between any two resources and P. Thapar (B) · L. S. Sharma Department of Computer Science and IT, University of Jammu, Jammu, Jammu and Kashmir, India e-mail: [email protected] L. S. Sharma e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_9

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hence is a better way to interchange data. The Semantic Web Technologies like RDF (Resource Description Framework), OWL (Ontology Web Language), and SPARQL (SPARQL Protocol and RDF Query Language) ontology are the basic building blocks of Semantic Web [3, 4]. RDF’s purpose is to add machine-readable metadata to web resources. Semantic web purposes the inclusiveness of unstructured documents to the web of data with RDF. RDF comprises of triplet relationship about an object, i.e., in the form of subject-predicate-object [5] whereas SPARQL (SPARQL Protocol and RDF Query Language) is the query language of the semantic web specifically designed to query data across diverse data sources [6]. OWL is the schema language that is used for knowledge representation in the Semantic Web and can be published in the World Wide Web through OWL documents known as ontologies [7].

1.1 Web Services and Its Requirements Web Services (WS) are loosely coupled applications that promote the reusability of code and support interoperable machine-to-machine interaction over the network. The self-contained description of WS allows organizations to publish and deploy their application components in distributed environments [8, 9]. The stack of Web Service consists of Web Service Description Language (WSDL) [10], Simple Object Access Protocol (SOAP) [11], Universal Description, Discovery and Integration (UDDI) [12]. The Combination of SOAP+WSDL+UDDI along with the following components defines a general model for Web Service architecture [13]: Service Consumer: It defines the user of a service. Service Provider: Entity that actually implements the service using WSDL interface. Service Registry: It’s the central place where Web Services are advertised for lookup. These technologies allow manual inspection and integration of Web Services for their usage, i.e., for an application, a developer searches a Web Service manually from UDDI repository that provides the desired functionality and then inspects the WSDL description to find the capabilities and type of information need to be interchanged with the Web Service and then finally integrates the SOAP message handling in the application. The requirements and preconditions of Web Services are defined as [14]: Web Service (ws): ws = {WSName, Interface, Capability}. • WSName represents the name of web service. • Interface = {Input, Output}, it denotes the input and output set of Web Service through service provider interface. – Input = {Ini , Ini ∈ Class, i = 0,1,…,inum} – Output = {Outo , Outo ∈ Class, o = 0,1,…,onum}

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• Capability = {Precondition, Effect}, it indicates the prerequisite using WSDL for service execution and defines the effect after ws execution. – Precondition = {Precp , p = 0,1,…pnum}, – Effect = {Effe , e = 0,1,…,enum}, Input, Output, Precondition and Effect are called as IOPE.

1.2 Semantic Web Services and Its Framework Besides, the Web Services architecture uses XML as their data model; the knowledge must be represented unambiguously for interoperability of web services in heterogeneous environments. To solve this problem, Semantic Web Services (SWS) use ontologies to add semantics in Web services and thereby support their interoperability, automatic discovery, composition, and execution in large open environments [15–17]. Two conceptual models are required to work with available Semantic Web Services namely OWL-S (Web Ontology Language for Services) which uses description logic (OWL) for service ontology [18] and WSMO (Web Service Modelling Ontology) that allows loose coupling of services using mediator architecture [19]. OWL-S allows the automatic discovery and composition of Web services via three ontologies namely ServiceProfile to describe the capabilities of services, ServiceModel for the data flow and control flow description, and ServiceGrounding to enable the interaction between services using message units and protocols. On the other hand, WSMO is based on meta ontology of four main concepts, i.e., Ontologies, Mediators, Services and Goals to semantically specify a web service. We have chosen, OWL-S web services for discovery and composition in our research proposal because it has more expressiveness than the latter one and has enough inference engines to derive new knowledge [20–22]. The remainder of this paper is divided into the sections described as follows: Sect. 2 discusses the literature that has been reviewed for our study while the motivation of our proposal is to answer the research-related questions explained in Sect. 3. Section 4 presents the research methodology on which the experimentation of our work was based. Section 5 discusses the results and analysis drawn from the experimentation. Finally, in Sect. 6 we conclude the paper with future plans for work.

2 Literature Review Research on web services comprised of various operations like their selection, composition, description, execution, discovery, ranking, etc. When a single atomic web service is failed to satisfy the user’s requirement, composition of web services

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combines compatible atomic web services to satisfy the user’s requirement [23, 24]. A significant research is being carried out for enhancement of discovery engines and composition of semantic web services. Some of the hybrid matchmakers work on logic and non-logical parameters of OWL-S services in [25, 26] to calculate semantic similarity metrics i.e. Jensen-Shannon, loss-of-information, and cosine similarity while the matchmaker implemented in [27] works on WSMO services with a test collection of 97 services in WSML-TC2 (Web Service Modelling Language- Test Collection). The main shortcoming that has been observed in these matchmakers was their more time consumption for logic-based matching and retrieval of more irrelevant services during discovery. However, to overcome the first shortcoming of more time consumption, [28, 29] implemented a cache mechanism to store frequently used services using graph of Semantic Discovery Caching (SDC). The formal description of preconditions and effects of IOPE using first-order logic was used by authors of [30] to fire user queries on OWL-S dataset. SPARQL language was used for this purpose and the proposal was compared with [25] in SME2 tool [31]. The main shortcoming of this work is that out of 1083 OWL-S services only 160 services have the formal description of PE (preconditions and Effects). Some approaches use hierarchical categorization, equivalence relation of concepts in [32, 33] based on the domain ontologies of composite schema. The non-functional parameters were used in these approaches to discard the irrelevant services if requested and advertised service belong to different domain categories. Approaches in [34, 35] worked on preprocessing of functional parameters to match few or all concepts using SPARQL language before actual discovery. However, [33–35] lacked their own matchmakers for discovery. Some more recent work in [36–38] also uses non-functional parameters like QoS for an optimal solution of composition. In [36], the composition method was based on k-means clustering to reduce the composition time of atomic services for better QoS parameters whereas a novel approach using Biogeography-based optimization (BBO) algorithm was implemented in [37]. The algorithm optimized the services discovery by repetitive improvement of the candidate services using availability and reliability of QoS as positive features and response time as negative feature. In [38], a semi-automatic approach based on formal and relation concept analysis has been implemented to select quality-based optimal web services. Also, in case of failure, no alternate composition solution was retrieved. With the increase number of semantic web services and domain ontologies, their discovery and composition is a new and challenging work for industries and organizations. However, the continuous work and efforts were done to improve the composition with more accurate results. The improvement in semantic matching of concepts based on ontologies also improves discovery and composition. Knowledge graph (KG), the semantic network is used in many domains like finance, entertainment, healthcare, cybersecurity, etc., to capture the semantic relationship [39]. The big knowledge graphs like Google Knowledge Graph with 570 million entities, Geonames with 25 million geographical entities and features, FactForge, and many others have been created for big Linked Open Data. To infer the correct and consistent entailments from this graph structure, different reasoning techniques like Rule-based,

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Neural network-based reasoning, and Distributed representation-based reasoning were implemented [40, 41]. Since, ontologies are the core model of Semantic Web Services, the same graph-based structure KG can be used in SWS for better results and reasoning during composition.

3 Research Questions The proposed research mainly focused on the selection of Web Services, and composition from the aspect of quality of service (QoS). The major challenges that were addressed in the implementation of a framework for functional composition of multiple Semantic Web Services are as follows: (1) How to find the best parameterized Web Services when many of them have similar functionality? (2) What factors influenced the selection of Web Services? (3) How to improve composition of multiple Web Services when a single Web Service is not able to satisfy client’s request?

4 Research Methodology The general-purpose approach is aimed at integrating heterogeneous Web Services through composition. We have focused mainly on automating the discovery process of SWS and add more semantically interlinked knowledge of different domains for further optimization of their performance during discovery. The proposed approach supports a methodology consisting of the following fundamental modules.

4.1 Preprocessing Module For the interaction of heterogeneous entities, ontologies were used as knowledge bases to provide interoperability. KG include the semantic knowledge of their mappings to define the semantics used in the service ontology. Then, the suitable candidates were discovered by inference from the service repository for composition and validated against the user’s query.

4.2 Creation of Knowledge Graph (KG) APACHE FUSEKI server was used to create triple store database from OWLS-TC version 4 [42] consisting of 1083 Web Services. The server was based on Semantic

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Web Query language SPARQL and use IRI (International Resource Identifier) of services for query implementation. The services and ontologies were parsed using JENA and stored in the form of composite triplet database as owls4dataset.ttl. The data was then imported in Python to create Knowledge Graph (KG) of 1.5 lakh triplets of ontologies and their service description methods using rdflib, networkx libaries.

4.3 Discovery Phase The discovery phase of the framework is shown in the steps of the methodology in Fig. 1. The input/output concepts of the user request were retrieved and queries were fired against the knowledge graph where the URI (Uniform Resource Identifier) of the ontology for each input/output concept was retrieved. To obtain the URI for subclass concepts tags of and tags were used. These URIs were used to retrieve all the direct and indirect subclass concepts from the KG. Then, these concepts were again fired as new inferred input/output concepts of user query. Based on the predicate of triplet if it is a valid service:Service, all the services where and tags matched with new retrieved subclass concepts were added in the relevant service list. All these services were validated against the relevance file given by the domain expert. The query given below was based on matching all the input/output and inferred concepts of user request: • Qall (Ss , Uq ) = {S' ∈ Ss : CUq ⊆ CS' } where: – – – –

CUq : describe the concepts/subclass concepts of the user request, CS' : describe the concepts of retrieved service descriptions from KG, Ss : service descriptions in the KG and S' : discovered service descriptions.

5 Results and Analysis This section explains the results obtained from the implementation of proposed work on KG generated from secondary dataset OWLS test collection version 4 of 1083 services and 48 domain ontologies which mainly covered 9 domains. These domains are geography, medical care, weaponry, food, education, travel, communication, economy, and simulation. The collection has 37 test queries from these domains that were parsed in the form of triplet to fire queries over KG. In [34, 35], semantic web query language was used for pre-filtering of domain-wise SWS but the approach was implemented on existing matchmakers for discovery and the query was used for domain-based atomic services. The reasoning entailment process of interlinked data was also missing whereas our approach was more generic and used composite big

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Preprocessing Module Export Machine Readable search results as JSON or RDF/XML for Discovery from KG (owls4dataset.ttl)

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User Request U

r

Fetch the input/output from USER Request

Steps to fetch the URI of input/output concepts Knowledge Graph1 Service Descriptions has >1 lakh triplets

Find subclasses of the concepts from Owls4dataset.ttl

Query the Knowledge Graph using “exact” degree of semantic match

List of relevant services Domain Expert

Export Results as Csv file1 Relevance File Steps to find Relevant services and Calculate Precision

Comparison and Observations

Fig. 1 Research methodology

interlinked KG data for knowledge discovery. Based on the number of relevant and irrelevant services retrieved, the average precision value and query response time are given in Table 1 for 37 queries. Average query response time denotes the average time taken to discover the service descriptions name and reasoning entailment time for each query. Lower precision value of 79.87% is due to some irrelevant concepts that were retrieved using subclass inference. The relation “subclass” is antisymmetric i.e. if “Luxury hotel” is subclass of “hotel” does not imply “hotel” is subclass of “Luxury hotel.” This will generate more false positives during reasoning entailment over KG. Also, the triplet used “subclass” as predicate to find all the subclass concepts during reason over big KG and thus increase the overall query response time. A sample test case is explained in the next section. Table 1 Results obtained for 37 queries Result

Average precision (%)

Average query response time (in seconds)

79.87

2.02 s

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5.1 Reasoning Over KG of OWL-S Services The KG of OWL-S services ontologies has been created and comprised of more than 1 lakh triplets. A sample knowledge graph of 900 triplets has been generated using WEBVOWL interface as shown in Fig. 2a. To test the inferred knowledge from KG using its inference engine, a test query has been implemented with “Book” as Query Input and “Price” as Query output. The list of concepts that has been retrieved using direct subclass inference for input concept “Book” are given in Table 2. Out of 6 concepts, 2 concepts are false positive because they do not have direct subclass relation with the input concept as shown in Fig. 2b. The outward arrow shows the “superclass” relation where the inward arrow shows the “subclass” relation. The services retrieval using subclass relation retrieve all the services with “subclass” as predicate of the triplet considering the graph as undirected graph.

Fig. 2 a Sample KG of 900 triplets; b Valid direct inferred concepts from KG input concept “Book”

Table 2 An example of direct subclasses for “Book” concept S. no

Input concept “Book”

Validity

1

http://127.0.0.1/ontology/books.owl#Encyclopedia

Yes

2

http://127.0.0.1/ontology/books.owl#Novel

Yes

3

http://127.0.0.1/ontology/books.owl#Short-Story

Yes

4

http://127.0.0.1/ontology/books.owl#ScienceFictionBook

Yes

5

http://127.0.0.1/ontology/books.owl#Hard-Cover

No

6

http://127.0.0.1/ontology/books.owl#Paper-Back

No

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6 Conclusion and Future Scope Experimentation results showed that reasoning over KG can be used for semantic web services composition but due to increased size of big linked open data, scalability is still the main concern during their discovery. The inferred results give some false positives that can be improved by implementing more restrictions, ranking, and consistent embeddings on Knowledge Graph. The new area of research [43, 44] on Knowledge graph used it as input model for Machine Learning on heterogeneous graphs using Graph Representation Learning. In future work, the concept of Machine Learning on knowledge graph will be extended to Semantic Web Services to envision their discovery and composition with more accurate results. Machine learningbased embeddings will be used for knowledge discovery and link prediction during semantic similarity of concepts.

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Fake Image Dataset Generation of Sign Language Using GAN Anushka Kukreti, Ashish Garg, Ishika Goyal, and Divyanshu Bathla

Abstract The massive boost in the technology industry proves as a boon for data science researchers. In the past few decades, new emerging advancements in the field of ML, Deep learning, and image pre-processing makes it possible to automate the task of different fields which required humans rather expert humans’ involvement. Since the advent of neural networking, there has been an enormous demand for acquiring information and generating datasets to support various research projects and to train module work for supporting artificial learning, but due to the lack of diversity in datasets, there are major hurdles during data preparation. There is a need to develop a system that could provide the visual dataset for this communication learning for machine algorithms. To overcome this bottleneck of the neural network, GAN (Generative Adversarial Networks) were created to acquire fake data with the objective of anonymizing users’ information to generate a huge stack of data representations. But working on GAN is not an easy task because it requires a deep understanding of deep learning and image pre-processing. In this paper, a dataset is included to support work for the deaf community which uses sign language to communicate. The task is to read hand signals by detecting the movements and visualizing the communication mode through movements of fingers and to achieve that, personal photos have been captured which are provided as input samples to GAN based model. The various requirements of future work in the same field will require a vast amount of hand sign data which is lacking in the current domain as there have been limitations with different backgrounds, shortage of volunteers, and different gestures to portray generalized reading and storage of such data in a variable format of hand signs have been raised as an issue by many research workers. This data exists in mere thousands and scrapped way over the digitalized media so a methodology is provided to resolve this problem through a tool that will create as many amounts of images datasets as required and, this will provide a healthier feed for machine

A. Kukreti · A. Garg · I. Goyal Computer Science, Graphic Era Deemed to Be University, Dehradun, India D. Bathla (B) Computer Science, Graphic Era Hill University, Dehradun, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_10

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training that will support future as well as current data requirements for work going on for the deaf community. Keywords Deep learning · GAN · Image pre-processing · Machine learning

1 Introduction As the vision of compact and sophisticated. development environment deepens the concept of learning through real-world entities, which are made to be paired and fitted to the generative statistical model of machine learning algorithms, there seems to arise a scarcity of datasets to support the gradient development of these background systems especially more towards the visual demonstrative image or video generation. Many AI and ML-based applications required a lot of data to perform well and provide good accuracy. In a few years, GAN has proven its impressive generating capabilities and it has been widely used to generate datasets for machine learning algorithms [1]. It is only capable of generating images that can be accepted [2]. In the past few years, GAN and its variants are also used to provide privacy and security in different domains such as privacy of image data, video data, Textual data, speech data, Spatiotemporal data, Graph data, etc. [3]. A GAN mainly has two components one is the discriminator, which is used to classify the images generated by the generator as real or fake and another one is the generator which attempts to generate fake images that can be discriminated by the discriminator into real ones. GAN transforms the image into an array, and each entry in the array represents a particular sample as each entry contains pixel information [4]. represents the pixels of an image. To generate an image dataset GAN usually adds noise to the given dataset and adding a lot of noise is not preferred by the researcher as adding high noise can work well for the maximum likelihood approach but can be incorrect for the problem [5, 6]. One major drawback of GAN is when data that belongs to different classes is given to the GAN model, the generated data contains characteristics that belong to different groups. Features vary when data belong to different clusters. Even though there are many GAN variants (e.g., Least Square GAN (LSGAN) [7], Wasserstein GAN (WGAN) [6], Improved WGAN [8] and DeLi GAN [9]), their generating approach is like the original GAN model, and encounters the same obstacle of unable to categorize mixed features from different classes. To increase the quality of generated data various modifications are done such as His-GAN which integrates histogrambased measurement score into the training of the GAN model to revise the generator’s parameters with back-propagation [10]. Major contributions of this paper include: 1. Generating a dataset with different hand signs and backgrounds from fewer images to as many as required to the algorithm with minor as well as major differences would resolve the underfitting issues occurring during the processing of hand sign data because of the unavailability of required amounts of data.

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2. Providing dataset of sign language using GAN where instead of directly giving image dataset as train data to GAN, image dataset is converted into heat map for better feature extraction and to decrease processing time.

2 Literature Review Deep learning Networks (DLN) have been fast-paced research strides for hard problems of discernment and learning. GANs are promising networks as the future assets in this direction as they are trailed both in business enterprises as well as academically for research work. The understanding of this one-dimensional resulting problem data for both academic and industry-related application development are well scoped in the immediate scope by Dutt et al. [11]. Three major components of SRGAN and ESRGAN and RRDB were studied as components of perceptual loss, adversarial loss, and network architecture of the same for improving visual quality by Xintao Wang et al. The suggestion in the research was to use intuitive loss features before the application of the activation function so that regularity of illumination can be prolonged, and texture can be preserved and recovered [12]. The problem with data sets that consist of multiple dimension features such as visual speeches and visual images was discussed by Yashwanth studied distinctive approaches to using GANs in actual-time packages and distinctive ways to apply GANs. GANs are critical for generating new statistics from existing records. Machine learning models do not work well when the dataset size is small. GANs exist to assist with scaling by creating new fakes from the original. GANs are used to create pictures from given phrases, which is a text-to-picture transformation. GANs had been carried out to picture decisions, picture transformation, and many other scenarios. The authors hope to apply this research to find out what extraordinary GAN applications exist and what their scope is. The writer also cantered on gaining knowledge of approximately the special sorts of his GANs presently to be had. In the last few years, the popularity gained by GAN leads to the improvement of its improvement and various feature of GAN has been proposed by various scientists [13]. Aghakhani et al. designed FakeGAN specifically for text classification from reviews and the model was able to identify misleading reviews. FakeGAN unlike GAN, which uses generators and discriminators, but FakeGAN used two discriminator models and one generator model. The discriminator was trained using the Monte Carlo search method and the generator model uses reinforcement learning (RL) to train the generator and proposed model conquers the problem of modal collapsing because the two discriminant models and generator model are trained based on true and false ratings [14]. The studies regarding the relationship between parallel intelligence and GANs were given through analysis of real virtual interaction with parallel integration systems which were handled by Gan’s algorithmic support

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mechanism for this process as they generate infinite samples best suited for speech processing, Information security, and IP fields as concluded by Wanget et al. [15]. A ModularGAN architecture that was proposed by Choi et al., used different functions including decoding, encoding, and transformation effectively working on multiple domains for generating images and for image translational issues [16]. Medha et al. [17] proposed a multispectral image demosaicking method that is based on weighted bilinear interpolation that can be used with images having multispectral bands. Karras et al. proposed an alternative GAN architecture that can be trained by human facial attributes such as pose, identity, hairs, and pigmentation that was able to successfully able to distinguish between the input human faces and generated humans [18]. Deng et al. proposed a semi-supervised model after studying problems arising in conditional generative modeling which were built on semantic and structured images. The authors profess that semantic-based image samples that were created by generators were unable to work with other models as they were not able to create the condition-based generators effectively [19]. Work presented on the analytic framework by Bau et al. helped in visualizing and understanding the collaborative level of GANs with respect to unit, object, and scene. The results were based on the concept that signals correlated with objects are observed to have output driven through the causal effects of synthesis objects. GAN is also capable of generating several views identity-preserving i.e. 3-D image processing [20]. Jie et al. proposed a model which was built on DCGANmethod that was able to generate 3D photos successfully and to achieve the result, a face normalizer and editor are used as generators and comparable discriminators were cast off as discriminators [21]. Mirjalili et al. proposed an ACGAN-based model to mask the gender information for privacy protection in which he used Autoencoder to generate fake images and a classifier discriminator consisting of many different classifiers to distinguish between input and generated images and used a gender classifier to encapsulate, gender attribute of face image [22]. Qinya et al. suggested the PicPrivacy model classify and remove delicate information, such as personal depiction from street view images, and create fake images without any human portraits to ensure the privacy protection of individuals. GAN also proves its uses in the field of security and can also be used as an encryptor [23]. Dule Shu et al. uses GAN to encrypt data in steganography and successfully perform transmission in wireless channels [24]. Jihoon Yang et al. proposed a model which was built on DCGAN anonymization privacy-preserving adversarial protector network (PPAPNet) that transforms a sensitive image into an attack-invulnerable synthetic image [25].

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3 Methodology The advancement of deep learning in the current era increases the demand for datasets in the community of Data scientists. Some algorithm especially image processingbased algorithms require a lot of data to train the machine so that it generates considerable results. To overcome this process an algorithm Gan was introduced in 2017 [26]. This algorithm can provide a fake image dataset which has look like the original image dataset. In this paper, authors are proposing a GAN-based model which can generate a fake image dataset of sign language used by deaf people for the community to work on it. The dataset generated by the Gan and various steps used to achieve the Fake dataset are shown in Fig. 1.

3.1 Data Collection The images of sign language used by deaf people are self-created by the authors and references of different images are taken by the public datasets.

3.2 Pre-Processing and Heat Map In this paper images dataset are pre-processed and converted into heat-map. Heat-map is a data visualization method that converts the image into magnitude of phenomenon by taking each pixel intensity consideration. The advantage of converting into heat map is heat map provides variation in colour based on intensity which helps machine to cluster them easily and eventually it increases the accuracy and decrease the training time of model.

Fig. 1 Workflow of proposed model

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3.3 GAN Algorithm Generative Adversarial Networks is a neural network-based om the methodology of unsupervised class of machine learning and deep learning. This generative model generates unique samples like CNN (Convolutional Neural Network). It is a system that uses the procedure of data generation through learning patterns and discovering generality from the input stream, this model is the estimated difference, yet similar object generation based on the real dataset. It trains and develops the supervised learning task through two sub-models. These sub-models include a generator and discriminator where generator model is trained to generate fake videos or images as samples by processing and learning from the real data provided as input and the discriminator model will try to identify and classify them into real and fake images. Equation of GAN as given by Goodfellow et al. [26] ) ( min max V (D, G) G

D

= min(max D (E x∼Pdata (x) [log D(x)] + E z∼Pz (z) [log(1 − D(G(z)))])) G

(1)

3.4 Fake Image Dataset The resultant fake image dataset has pics like real image up to an extent that discriminator is not able to distinguish between them.

4 Proposed Model In this paper, authors are attempting to create a model which can generate a fake image dataset for the sign language used by deaf people.

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4.1 Algorithm of Proposed Model

ALGORITHM: Fake image generation 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

def function data Input dataset train_data heatmap(data) noisy_data data+random noise gen_img Generator(noisy_data) dis_output Discriminator(train_data,gen_img) prob Loss_func(dis_out) If prob 0.40 then Stop and use generator to generate image dataset else back propagate to step 5 and 6 end of function

4.2 Working of Proposed Model The input dataset of the model shown in Fig. 2 contains the image dataset of sign language which after pre-processing is converted to heat map and that heat map is used as a training dataset to train images. Then training dataset is passed to two different neural networks i.e., generator and discriminator which uses sigmoid and tanh as activation function respectively. Before giving dataset directly to the generator each image is added with the noise. After getting the noisy images generator generate some fake images which are also given as input to the discriminator along with the training dataset and discriminator model generates a report to the generator about the classification of the real or fake images performed by back precogitation. It acts as a binary classifier and predicts 0 to 1 confidence scores with respect to real or fake images. The output of the discriminator is also given as input to loss function to calculate the error. During the training phase, there is a huge competition between the two submodules namely Generator and Discriminator. In the training or learning phase, many repetitions will be performed. After each iteration, the generator and discriminator enhance their performance since the discriminator tries to minimize the error rate while the generator tries to maximize discriminator errors. Finally, after getting close accuracy results, the discriminator is discarded while generator is used to obtain the image output for our needs.

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Fig. 2 Fake image generator model

5 Experiment Setup 5.1 Dataset Description The experiment for the model is performed by self-created dataset of images captured during performing the sign gestures and being fed to the model and self-created dataset wis divided into 36 sub-directories where each directory 1–10 contained the images of sign languages which denotes numeric hand signals from 0 to 9 as shown in Fig. 3. Fig. 3 Sign language to show number 3

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Fig. 4 Sign language to show alphabet D

And the directories ranging from 11 to 36 contain the hand images of signs representing alphabets from A to Z as shown in Fig. 4, each sub-directory containing at least 500 images of the sign gesture as per the directory name.

5.2 Experimental Testbed The proposed model was tested using Windows 10 with software having intel core i7 processor running on a 16 GB ram with a 64-bit system type. The model was created using Anaconda environment supporting the Jupyter notebook for testing results, the images resulted through the Gpu system of Nividia Geforce GTX 1660Ti. We have used various standard library such as TensorFlow, NumPy, PLT, Matplotlib, etc. Python has been used throughout this research work because in python the development and implementation of desired logic is faster in python as compared to other languages.

6 Results After preprocessing the image dataset and extracting important features of images using heat map the image have been added with random noise and noisy data has been created. In our experiment we are using the noise of 100 matrix size and noisy data is further given to the generator and model is trained multiple times until discriminator is unable to distinguish between real and noisy dataset. Result was observed at different training levels as shown in Figs. 5, 6 and result after multiple epochs until discriminator is unable to differentiate between real and fake images are shown in Fig. 7. After 20 epochs generator can be used to generate as many images as required. In epoch 20 the discriminator loss is 0.4826 and the generator loss is 2.4354 which is quite good as the discriminator loss is less than

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Fig. 5 Result after 10 epochs

Fig. 6 Result after 15 epochs

Fig. 7 Result after 20 epochs

0.5 that means generator is generating images that are close to real images. So, the experiment’s results show that images generated by the model after 20 epochs can be considered for further evaluation and generation of fake image dataset.

7 Conclusion and Future Work The proposed model is the extension of GAN where authors have attempted to generate the dataset of sign language after converting into heat map for data science community and result of the proposed model provides considerable image dataset which can be used for further analysis of sign language.

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The generated dataset can be used to train model to detect sign language and interpret it to Global language and regional languages in real time and to provide an integration of software and hardware-based solution of the problem faced by deaf people which can be left as a future work.

References 1. Li C, Wang S, Yu PS, Zheng L, Zhang X, Li Z et al (2018) Distribution distance minimization for unsupervised user identity linkage. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 447–456 2. Cai Z, Xiong Z, Xu H, Wang P, Li W, Pan Y (2021) Generative adversarial networks. ACM Comput Surv 54(6):1–38. https://doi.org/10.1145/3459992 3. Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35(1):53–65 4. Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training gans 5. Wu Y, Burda Y, Salakhutdinov R, Grosse R (2016) On the quantitative analysis of decoder-based generative models. arXiv preprint arXiv:1611.04273 6. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein gan. arXiv preprint arXiv:1701.07875 7. Mao X, Li Q, Xie H, Lau RYK, Wang Z, Smolley SP (2017) Least squares generative adversarial networks. In: Computer vision (ICCV), 2017 IEEE international conference on, pp 2813–2821. IEEE 8. Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of wassersteingans. In: Advances in neural information processing systems, pp 5767–5777 9. Gurumurthy S, Sarvadevabhatla RK, Babu RV (2017) 10. Li W, Ding W, Sadasivam R, Cui X, Chen P (2019) His-GAN: a histogram-based GAN model to improve data generation quality. Neural Netw. https://doi.org/10.1016/j.neunet.2019.07.001 11. Dutt, Premchand (2017) Generative adversarial networks (GAN) review. CVR J Sci Technol 13:1–5. ISSN 2277-3916 12. Wang X et al. ESRGAN: enhanced super-resolution generative adversarial networks. ArXiv: 1809.00219[Cs], pp 5–22 13. Yashwanth N et al (2019) Survey on generative adversarial networks. 5(2):239–244 14. Aghakhani H et al. (2018) Detecting deceptive reviews using generative adversarial networks. ArXiv:1805.10364[Cs], pp 1–7 15. Shrivastava A, Pfister T, Tuzel O, Susskind J, Wang W, Webb R (2017) Learning from simulated and unsupervised images through adversarial training. CVPR 16. Choi Y, Choi M, Kim M, Ha JW, Kim S, Choo J (2018) StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. CVPR 17. Gupta M, Ram M. Weighted bilinear interpolation based generic multispectral image demosaicking method. J Graphic Era Univ. ISSN: 0975-1416 18. Karras T, Laine S, Aila T (2018) A style-based generator architecture for generative adversarial networks. ArXiv:1812.04948[Cs, Stat] 19. Deng Z et al (2017) Structured generative adversarial networks. ArXiv:1711.00889[Cs] 20. Bau D et al (2018) GAN dissection: visualizing and understanding generative adversarial networks. ArXiv:1811.10597[Cs]. Accessed: 2 Sept 2019 21. Cao J, Hu Y, Yu B, He R, Sun Z (2019) 3D aided duet GANs for multi-view face image synthesis. IEEE Trans Inform Forensics Secur 14(8):2028–2042 22. Mirjalili V, Raschka S, Namboodiri A, Ross A (2018) Semi-adversarial networks: convolutional autoencoders for imparting privacy to face images. In: Proceedings of the 2018 international conference on biometrics. IEEE, Los Alamitos, CA, pp 82–89

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23. Li Q, Zheng Z, Wu F, Chen G (2020) Generative adversarial networks-based privacypreserving 3D reconstruction. In: Proceedings of the 2020 IEEE/ACM 28th international symposium on quality of service. IEEE, Los Alamitos, CA, pp 1–10 24. Shu D, Cong W, Chai J, Tucker CS (2020) Encrypted rich-data steganography using generative adversarial networks. In: Proceedings of the 2nd ACM workshop on wireless security and machine learning. ACM, New York, NY, pp 55–60 25. Kim T, Yang J (2019) Latent-space-level image anonymization with adversarial protector networks. IEEE Access 7:84992–84999 26. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. https://doi.org/10.48550/arXiv.1406.2661

Analysis of Multimodal Biometric System Based on ECG Biometrics Sandeep Pratap Singh and Shamik Tiwari

Abstract Biometric qualities have attracted a lot of study interest over the last few decades. Some traits, such as face and fingerprint, as well as iris, are in recent years the most widely investigated biometric traits in a variety of applications. However, as newer strategies for imitating such features emerge, modalities that are invulnerable to stealth or spoofing attacks are required. This allowed a new biometric trait, the electrocardiogram (ECG), to gain momentum, which is linked with medical diagnosis and is highly resistant to attacks due to its hidden nature and inherent liveness information. But Unimodal biometric systems are attached with some demerits such as Noise in Data, variation in same class, similarity in different classes and more. To counteract these flaws of unimodal systems (single biometric traits), we need a system that overcomes the limitations of any single model. Therefore, multimodal biometric system by combining different biometrics features is required. Multimodal with ECG as one of the traits is the area we are exploring. As liveness detection of subject is not available for most of the modalities so we also need modality to support liveness of subject. We evaluate and discuss existing works in Multimodal biometrics, as well as their proposed methodologies, datasets. The information gathered is utilised to present advancement of ECG biometrics in a multimodal environment. Keywords Multimodal Biometrics · ECG biometrics · Ocular biometrics · Fusion · Fingerprint

1 Introduction Biometrics is the study of processing and interpreting biological data for the purpose of user identification or authentication. User recognition techniques are classified into categories based on their operational principle: (a) what the individual knows (e.g. pin); (b) what an individual possesses (e.g. identification card); (c) Individuals physical appearance (e.g. face); and (d) activities done by individual (e.g. gesture). S. P. Singh (B) · S. Tiwari School of Computer Science, UPES, Dehradun, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_11

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The final two approaches are often framed in the field of biometric recognition, which encompasses many forms of physiological or behavioural characteristics that are uniquely tied to an individual in the current state-of-the-art. Biometrics is the science of computing and analyzing distinctive behavioural or physical attributes that are used to identify a person. Physical attributes like—Fingerprint, Face, Iris, Retina, DNA, Ear and Hand geometry and are related to the shape, size or measurements of the human being Behavioural attributes like—Signature, speech and Gestures they are related to behaviour of an individual. Every biometric feature has its own set of advantages and disadvantages. For a specific authentication application, the relevant biometric attribute should be employed depending on the requirement. Physical traits are used by emerging technology applications to recognise humans. Nowadays, a wide range of applications rely on verification or identification to validate a person’s identity. Traditional passwords and identity cards have been used to safeguard systems by restricting access, but these can be readily hacked. The biometric systems have number of advantages over traditional authentication systems as biometrics cannot be, forgotten, or forged, borrowed, stolen Biometrics are now widely used in domains such as forensic science, cyber security, identification and authorisation systems. For the evolution of biometric systems based on fingerprints, voice, iris, face and other biometrics, a lot of research work is required, but new biometrics have lately been developed. The use of biological signals as biometric traits, such as the electroencephalogram (EEG) or electrocardiogram (ECG), is another field of biometrics that has gained interest in the previous decade [1–4]. ECG signal can be used to identify individuals. ECG-based biometrics, in comparison to other biometric systems, can be used by a wider spectrum of people, including amputees. A heart signal can be acquired by any part of the body (e.g., toe, chest, finger and wrist).

2 ECG as Biometrics A new range of biometric qualities known as medical biometrics has recently gained popularity. When compared to other biometric qualities, the electrocardiogram (ECG) has proven to be the most promising, matching features that are in line with the what is better for a biometric trait. Its basic nature makes it difficult to recreate and insert into the model for spoofing attacks, and the biometric system’s characteristic liveness quality assures that it is not compromised. Furthermore, because of its one-dimensional nature, it is a more effective option for computation to imagebased or video-based systems, particularly for identification systems that rely on quick judgements. Electrocardiogram acquisition settings are now acceptable and comfortable enough to be used in modern biometric systems, but they have introduced new concerns such as increasing signal noise and variability. Furthermore, academics have lately begun to investigate other deep learning approaches, which

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have the potential to improve robustness while also posing new obstacles of data availability. Electrocardiogram (ECG) is a biological signal produced from the cycle of contraction and the relaxation of the heart. Following part will address the Electrocardiogram Biometric motivation and challenges, anatomy and physiology of ECG, acquisition, public datasets [1, 5].

2.1 ECG Biometrics Merits and Prime Challenges The ECG has been shown to provide adequate detail for identification from a biometrics standpoint. The benefits of employing the ECG for biometric recognition can be summarised as follows: uniqueness, universality, robustness to attack permanence, liveness detection. More precisely [6]. Universality. It is naturally available vital signal in every human body. So it is universal. Permanence refers to the ability to match biometrics against templates that were created earlier. This essentially necessitates signal stability over time. Both psychological and physical activities can affect the ECG. Despite the fact that the pulses’ specific local qualities may vary, the diacritical waves and rhythm stay the same. Uniqueness: Because of its physiological origin, the ECG signal is guaranteed to be unique. While different people’s ECG signals have a similar pattern, there is notable inter-individual variability due to the many electrophysiological features that control waveform formation. Robustness to attacks: The ECG waveform’s appearance is the result of several parasympathetic and sympathetic components in the human body. To alter the waveform or attempting to imitate another person’s ECG output is challenging, if not impossible. There is currently no way to fabricate an ECG waveform and offer it to a biometric recognition system, to our knowledge. Liveness detection: Only living subject can have ECG waveform so it naturally satisfies liveness. Iris or fingerprint are such other biometrics that, necessitate further examination to determine whether the reading is live. Despite its benefits, this technology has significant hurdles when it comes to large-scale deployment: Collection periods: Unlike biometrics such as the fingerprint, face, ears, iris, where biometric information can be captured at any time, the ECG signal is not available for capture at every time instance. Every heartbeat takes around a second to develop, so expect longer wait times with this technology, particularly when extended ECG segments are needed for feature extraction. So the difficulty is to reduce the number of ECG waveforms that the recognition algorithm employs. Privacy issues: When acquiring ECG signals, a substantial quantity of sensitive data is invariably collected. The captured ECG signal can indicate existing and prior physical issues, as well as suggestions about the observed person’s immediate emotional activity. As a result, the prospect of matching ECG samples to identities could result in serious privacy concerns.

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Cardiac Conditions: Despite the fact that cardiac illnesses are not as common as damages for more traditional biometrics (fingerprint, face), they can limit ECG biometric approaches. One of the most researched conditions in ECG biometrics is arrhythmia, which produces significant fluctuations in heart rate over time and, according to various experts, can constantly hamper the effectiveness of ECG-based biometric models [6].

2.2 Physiology of the ECG The heart has: two atria and ventricles each. The venous circulation returns deoxygenated blood to the right side of the heart. It is pumped into the right ventricle before being transferred to the lungs, where it is used to expel carbon dioxide and receive oxygen. The oxygenated blood is then pumped into the aorta and arterial circulation before returning to the left side of the heart and entering the left atria [1]. The contraction of the heart has the most important role in generation of waveform. Electrical currents that generate depolarisation cause cardiac muscle cells to contract… Electrical currents are created and carried through the heart during these depolarisation and repolarisation flows. Electrocardiography is a procedure that detects and measures electrical currents in the body using electrodes implanted in the body. The output waveform is an electrocardiogram and, in normal healthy individual. It is a cycle of waves namely P, Q, R, S and T waves (see Fig. 1). All these deflections combined to form a single heartbeat, Although the ECG signal displays the same deflections for all participants at all times in normal settings, The link between the distinct heart cycle in an ECG signal and the order of contraction and relaxation events in the heart is highly variable. Intra-subject variability in the ECG refers to differences between cycles deflections of P, Q, R, S, (heartbeats) in the ECG of an individual, while inter-subject variability refers to differences between heartbeats of different persons. The intra-subject variability (difference in same person) of the ECG is studied for health observation and

Fig. 1 ECG waveform components [cf. 1]

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medical analysis, but inter-subject variability is particularly important in biometric recognition to distinguish between persons. Both these variations can occur due to many attributes, most specifically [1]. Heart Geometry: The route the electrical signal will take, the amount of myocardium muscle cells that will contract, and the time period it takes to do so over the entire heart are all determined by heart muscle width, heart size and the general form of the heart. Athletes’ hearts are often larger, with wider myocardia, which impacts the ECG with larger voltages in the QRS complex and lesser heart rates due to their intense physical exercise. Individual Attributes: Individual characteristics such as age, weight and pregnancy might induce changes in heart position and/or orientation. The direction of the electrical current conduction vectors through the heart will change as a result of these alterations, causing the electrodes to identify the signal from a new viewpoint, causing the ECG waveforms to change.

2.3 ECG Datasets There are currently various collections for ECG biometrics area that attempt to address some or all of these elements in order to make a difficult for the creation of strong biometric model. Physionet stores several of them, while their owners relinquish control of others. The most related of the currently accessible ECG sets are shown and described here. AHA: The American Heart Association, contains 154 ECG readings from actual patients, each consists of two lead signals for three hours long and, supplied by various institutes. Each recording’s last 30 min are marked for 7 diverse types of arrhythmia. CYBHi: It comprises two datasets: a short-term dataset with 65 volunteers recorded in a single session, and a long-duration dataset with 63 individuals recorded in two sessions with a difference of three months. The respondents were exposed to movies tailored to elicit emotional responses for 5 min in each session. ECG-ID: The database a contains a total of 301 recordings which ranges from 2 to 20 each individual collected in a difference of a half year. The recordings were obtained from Lead 1. MIT-BIH Arrhythmia, At the Physionet repository, you can find the MIT-BIH Arrhythmia database, which is quite popular in ECG-based biometrics research. It contains 48 signals, each 30 min time, extracted from ambulatory 2-lead readings. The 47 participants were chosen to represent a diverse range of arrhythmias. MIT-BIH Normal Sinus Rhythm: Dataset contains selections from 18 people found to be devoid of arrhythmias or other abnormalities, as determined by the MIT-BIH Arrhythmia database. PTB: The PTB ECG dataset has 549 readings from 290 normal people and people with a variety of heart problems. It contains 1 to 5 recordings per person, ranging in period from 38.4 to 104.2 secs, and includes 3 Frank leads and all 12 standard leads.

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3 Unimodal and Multimodal Biometrics Unimodal Biometric architecture: For authentication process, a unimodal biometric system focuses on a single biometric feature. It can use any single physical or behavioural biometrics trait. Unimodal systems normally have 4 segments. ● ● ● ●

Sensor segment Segment for feature extraction Segment for matching Storage segment.

Sensor segment senses and acquire one of the biometric features as the user input. The acquired input possibly an image is cleaned by preprocessing. Segment for Feature Extraction extracts the important information from the acquired details. Unwanted part is removed for better authentication. Only the required feature attributes are acquired. In the first phase, acquired details are put in the dataset. This is the task of the Storage segment. During authentication phase, matcher segment query data is matched with the already stored data for verification. Demerits of Unimodal Systems: Noise in Data, variation in same class, similarity in different class, universality issue, spoof attacks. Multimodal Biometric architecture: To determine an individual’s identity, multimodal biometric innovation employs more than one biometric marker. Unimodal biometrics frameworks are regarded to be less trustworthy and productive. If one of the traits cannot be determined, the user can be authenticated using another marker. It also defends against spoofing attacks by employing template protection techniques such as watermarking, fuzzy vaults and cancellable biometric cryptosystems, among others. Multimodal biometric system contains of five segments such. ● ● ● ● ●

Sensor segment Segment for feature extraction Fusion segment Segment for matching Storage segment

Sensor segment is same as unimodal system here additional sensor is used for extra biometric identifier. The acquired inputs possibly images are cleaned by preprocessing. Segment for Feature Extraction extracts the important information from the acquired details. Fusion segment is used to fuse combine the multiple biometric features acquired from different traits. Matching segment and storage segment feature is same as unimodal the only difference is it takes fused template as input... Fusion types-feature level fusion, score level fusion, rank level fusion, decision level fusion. 1. In feature level fusion, the details obtained from different modalities have been combined into a single template.

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2. In matching score level fusion, scores calculated from various classifiers for different modalities are merged and those having the higher score are considered 3. Decision level fusion combines the outputs of matching segments.

4 Multimodal Biometrics (Two Modalities) Fingerprint iris: One of the combination for multimodal is Fingerprint and Iris. Fingerprint is most widely used, reliable and traditional biometric trait. In [7] paper these two modalities can be fused at score-level, using z-score standardisation, scores are combined using approaches like min max score, sum rule. In [7, 8] feature level fusing techniques are used for multimodal biometrics. Ocular trait Iris is also gaining popularity which has proven accurate and contactless biometric technology. Face Iris: Another combination for multimodal biometrics are Face and Iris. Iris is one the most accurate biometrics unaltered throughout life and can be segmented from eye images. Face is also the simplest way to recognize a person, low cost identification. Another reason for combination is high resolution face image can give you both Iris and face features. Moutafis et al. [9] used eigenface for facial features and Daugman’s algorithm for iris features. Researchers in [10] used 1D Log-Gabor filter for Iris. PCA, LBP are applied to extract facial features for selection PSO, Score and feature level fusion, tanh normalisation and weighted sum rule are used. In [11] PCA, DCT for face and 1D log gabor, genetic algorithm for IRIS are used. Hybrid fusion and multi resolution 2-dimensional log gabor filter is applied by B Ammour et al. [12]. Face Ear: Face and Ear can also be combined for multimodal biometrics as both are contact free, can be captured by same type of sensor, ear are independent of facial expression, these are some reasons to combine. Both can be combined using score level fusion, feature level fusion, decision level fusion, rank level fusion and sensor level fusion. Chang et al. [13] used sensor level fusion using principal component analysis-based algorithm for feature extraction. Yuan et al. [14] used FSLDA, Yaman et al. [15] used CNN based method. Xu and Mu [16, 17] merges face and ear features at feature level. They achieved enhanced accuracy with the help of Kernel CCA (canonical correlation analysis) feature fusion technique. Theoharis et al. [18] suggested an integrated technique that merges 3D face and ear features. They created annotated face model (AFM) and an annotated ear model (AEM) with the help of statistical data. It attained good recognition rate. Huang et al. [19] proposed a multimodal technique named MSRC, it joins PCA features of ear and face in serial one after other and applied SR classification. MSRC performs better than CCA-based techniques. Score level fusion with its schemes like sum, product, median rule is used by, Xu and Mu [20], Yan [21], Mahoor et al. [22], Islam et al. [23], Yaman et al., Huang et al. [24], with some methods like FSLDA algorithm, ICP method, CNN models, sparsity-based matching metric.

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Iris Voice: In [25] Iris and Voice are fused at score level for multimodal system using min–max scheme. For feature extraction Gabor Wavelet and Hamming distance are applied.

5 Multimodal Biometrics with ECG (Two Modalities) One Numerous multimodal biometric systems based on conventional modalities such iris, fingerprint have been created during recent years; but only a few studies have been done on a multimodal biometric system that integrates ECG. Komeili et al. used an ECG with a fingerprint to identify and confirm liveness. They did not, however, emphasis on fingerprint authentication accuracy and instead concentrated on liveness verification [26]. Zhao et al. proposed a multimodal biometric architecture based on finger ECG and fingerprint readings, although they did not test it [27]. Manjunathswamy et al. devised a biometric recognition method based on ECG and fingerprints. To combine the ECG with the fingerprint, they employed decision level fusion. They did, however, work on a low authentication barrier (75%) and did not offer details on the study’s user count [28]. Singh et al. also described a multimodal biometric system, however the ECG data have been acquired from chest readings, ignoring one of the key benefits of a smaller finger based device [29]. However, all above focused on traditional machine learning algorithms, which frequently suffer from overfitting and perform poorly when verified on various other datasets. Zokaee et al. proposed a multimodal biometric system using ECG biometrics and palmprint, which they developed using principal component analysis for palmprint feature mining, mel-frequency cepstrum coefficients method for ECG features followed by the application of the k-nearest neighbours (KNN) classifier [30]. Fatemian (2009) proposed a multi-biometric identification method based on the combination of 2 heart-related features, ECG and PCG and the decision level [31]. Sim et al. (2007) developed a fusion method based on face and fingerprint recognition through modalities and time. The researchers introduced a HMM model-based mathematical model in a Bayesian framework, as well as the creation of a biometrics model that constantly confirms the presence of users those are log in [32]. Boumbarov et al. (2011) combined ECG and face biometrics using rules like minimum rule, maximum rule, summation rule, product rule, depending on each classifier’s output probabilities. For facial biometrics, the support vector machine was used and for ECG biometrics, the Radial Basis function network-based classifier was employed [33]. Several normalisation methodologies and the effectiveness of various rules based on hand shape size, fingerprint, and face multimodal biometric system were investigated by Jaina et al. (2005). To the multimodal biometric system, the researchers

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used the normalisation techniques z-score, min–max, followed by a summation of scores fusion method [34]. Pouryayevali (2015) proposed combining fingerprint and ECG modalities to create a multimodal biometric recognition method. Support vector machine (SVM), likelihood ratio, weighted sum rule and were used in the fusion system, which was based on cutting-edge techniques. For real-world applications, the fusion technique performs admirably and is resistant to spoof attacks [35]. They examined the performance of the multimodal biometric dataset for ECG, face and fingerprint modalities in Raju and Udayashankara (2017), who offered a different method for individual identification on the basis of dataset privacy using multimodal biometrics. Multimodal biometrics improve biometric individual authentication accuracy and security, according to the findings [36]. Shekhar et al. (2014) based on a multimodal sparse method proposed a sparse multimodal biometrics recognition (SMBR) approach, in which the test data is denoted by a sparse linear combination of training data [37]. Barra et al. (2017) demonstrated a multimodal biometric architecture that integrated the six different EEG bands with first ECG lead I. The EEG spectrum features and fiducial point features from the ECG were retrieved, and the fusion was done based on these features. The product, sum and weighted sum are multimodal system operators that are used on ECG signal and EEG band [38]. Sabri et al. (2019) suggested a biometric identification model based on sequential feature and workflow management using a range of classifiers. For dynamic individuals’ authentication, the matching on card method and matching on host processes are utilised. They used their architecture on a multimodal chimera database, which improves the accuracy of a conventional match on card [39]. Ching (2013) tested a novel mix of accelerometer-based gait modalities, speaker and face in a fused biometrics system. They built the MMU GASPFA (GAit–SPeaker–FAce) multimodal database, in which accelerometer-based gaits were gathered from people wearing various footwear, encumbrance and clothes to see how each aspect affected the identification process [40]. Meryem Regouid et al. 2019 proposed Multimodal system of biometrics based on local descriptors for ECG, ear and iris recognition. It one of few that uses 3 modalities [41]. Mohamed Hammad et al. 2018 used CNN Based on feature and decision level Fusion of Fingerprint and ECG, achieved good results. Mohamed Hammad et al. 2019 proposed model with ECG and fingerprint Parallel score fusion for human identity verification using CNN [42]. El_Rahman 2020 used different classifiers like NN, FL LDA for Multimodal biometric systems based on various fusion techniques (score, decision) of ECG and fingerprint [43]. Monwar and Marina et al. proposed a model, that merge face, ear, iris data. This uses Hough transform method and Hamming distance for iris recognition and the Fisher image method to the ear and face image datasets for recognition [44]. Analysis in tabular form is shown in Fig. 2.

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Authors and Year

Modalities

Fusion Techniques /Fusion Level

Sample size,Dataset used

Performance measurment

Remarks

Komeili et. al.(2018)

ECG,Fingerprint

Score based fusion

LivDet2015 database

average EER 2.9%

Only liveness detection concentrated not targeted on fingerprint authentication performance

Zhao et al.(2012)

ECG,Fingerprint

AC/LDA(autocorrelation/linear discriminant analysis) method

-

Did not evaluated

No evaluation

Manjunathswamy et al.(2014)

ECG,Fingerprint

Score based fusion

Biopac MP35 for different subjects used for ecg aquisition

Multimodal biometric: match rate up to 92.8% ,FAR = 2.5, FRR = 0

Low authentication threshold (75%),

Zokaee and Faez

ECG

score level Matching

data of 50 subjects

Multimodal: Acc. 94.7%

(2012)

palmprint

Fatemian

ECG and PCG

and

Palmprint: Acc. 82.1% ECG: Acc. 89% fusion Decision level

data of 21 subjects

(2009)

ECG: for 95% similarity accuracy of

Intrasubject

93% threshold

variability

%

issue

Multimodal: accuracy of 96.4% PCG: accuracy of 88.7% with a Boumbarov et al

ECG and face

data of 19 subjects

Likelihood

data of 45 subjects

Multimodal: EER of 1%

52

Good

. (2011) and

Pouryayevali

ECG

(2015)

fingerprint

sum rule, SVM

Barra et al

ECG and EEG

Score

. (2017)

certainty 70 Multimodal Minimum rule: Acc 99.0%,Maximum rule: Acc. 93.15% rule of sum: 99.1% rule of product: 99.5%

Max rule, Min rule , Sum rule ,Product rule ,Decision level,

ratio,

level,Sum

Weighted

,Product

,Weighted sum

subjects

data(EEG

Motor

results

over

basic

fusion

methods with different bands of EEG

Movement/Imagery

Intrasubject variabilty issue

Not

much

features explored

Dataset (EEGMI)and PTB database Sabri et al. (2019)

Fingerprint and

Score level,

200 subjects

No liveness

ECG

Classifier based decision fusion

MDB1 and MDB2

,Fingerprint

,Feature fusion with addition

face Mohamed Hammad

et

al.(2018)

Feature fusion with addition MDB1

simultaneous

EER=0.40%,MDB2=0.32%,Classifier

Intrasubject

based decision fusion MDB1 EER=

variabilty

0.14%,MDB2 EER=0.10%

issue+ Fingerprint issues

Sahar

A

Rahman(2020)

El

ECG

NN,LDA,FL (For score based

FVC

,Fingerprint

fusion),score

level

Fingerprint

fusion,decision

level

database

2004 ,47

SEM(Standard error of mean) is up to

simultaneous

0.00824 for decision level,, and up to

Intrasubject

0.01045 for score level,

variabilty

fusion,Max rule ,Sum rule

subjects data (MIT-

issue+

,Product rule.,

BIH database) and

Fingerprint issues

Fig. 2 Analysis with two modalities

6 Multimodal Biometrics with ECG (Three Modalities) In the previous section, we discussed research articles that are using two modalities with one modality being ECG. In this section, we analysed few articles that have explored the three modality field with one being ECG in it, Illustrated in Fig. 3.

Analysis of Multimodal Biometric System Based on ECG Biometrics Authors

and

Modalities

Fusion Techniques /Fusion Level

Year

Sample

127

Performance measurement

Remarks

size,Dataset used

Singh et al(2012)

ECG with face and Fingerprint

Transformation based score fusion, Weights on match scores distributions (FTMSD)Weights on equal error rate (FTEER) ,

MIT-BIH arrhythmia .+ST -T +MIT NSR+QT

EER=1.52%

a compact finger-based system is not used for ECG collection which may be the disadvantage

Jaina et al. (2005)

Fingerprint, face, and hand geometry

Max-score rule ,Min-score , Sum of scores

data of 100 subjects

Multi-biometric : Maximum-score: GAR up to 93.6% Minimum-score: 85.6 Sum of scores: 98.6%

No liveness

Raju and Udayashankara (2017)

ECG, face, and fingerprint

SVM-based , Likelihood ratio based ,,Weighted sum rule,

data of 50 subjects

--

simultaneous Intrasubject variability issue+ Fingerprint issues

Shekhar et al. (2014)

Iris ,fingerprint,face

Support vector machine (SVM) , Multiple kernel learning (MKL),Sparse multimodal biometrics recognition (SMBR) ,

219 subjects data (WVU multimodal dataset)

Multimodal SMBR-WE: 98.7 ± 0.5 SMBR-E: 98.6 ± 0.5 SVM-Major: 81.3 ± 1.7 SVM-Sum: 94.9 ± 1.5 MKL Fusion: 89.8 ± 0.9

No liveness

Meryem Regouid et al.(2019)

ECG, Ear and Iris

feature level

CASIAv1 iris databases ,IDECG and USTB1, USTB2 and AMI ear

0.54%, 0%, 1.1%,100%, for ERR, FAR and FRR, , CRR resp.

Iris may not work in certain deformity, Ear more prone to give error

Monwar et al.(2011)

Face, Iris and Ear

Rank level

CASIA database ,USTB database,(FERE T) database

--

No liveness

Fig. 3 Analysis with three modalities

7 Conclusion and Future Scope ECG Based Multimodal Multi-biometric frameworks are considered to be more reliable and productive than unimodal biometrics frameworks because multimodal will fulfil the drawbacks of single biometric model. As seen in Figs. 2 and 3 remarks each combination of modalities has certain parameter lagging either it be liveness, intra-variability issues, low authentication threshold, less feature explored. On the basis that certain gaps have been concluded and some novelty is suggested. 1. There are number of multimodal systems based on two modalities like EEG + ECG, ECG + Face, ECG + Speech, ECG + Fingerprint in all these cases if Intra-variabilty issues (cardiac condition) and failure of other modality occur simultaneously then system can be failed 2. There are also some multimodal that uses three modalities like Ear + Iris + Face, which fails to check liveness of subject, ECG + Iris + Ear this system will fail if Intra-variabilty issues (cardiac condition) and other modality fails like Iris (in case of contact lenses, potential eye redness, blindness) occurs.

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Table 1 Font sizes of headings. Table captions should always be positioned above the tables S. no.

Dataset

No. of subjects for reading

Collection period

Remarks

1

AHA

154

30 min per recording

–

2

CYBHi

65 (for short term) 63 (for long term)

Two sessions with difference of 3 months

5 min movie for emotional response in each session

3

ECG-ID

2 to 20 (301 recordings)

Collected in difference of half year

–

4

MIT-BIH arrhythmia

48

30 min each

–

5

MIT-BIH normal sinus rhythm

18

–

Based on MIT BIH arrhythmia

6

PTB

290 (549 readings)

38.4 to 104.2 secs

–

3. There is no Multimodal with a combination of ECG + Sclera + Fingerprint. It removes problem of two modality, also Sclera works better over Iris in existing three modal system So a novel biometric system can be proposed. In this system, we combine ECG, Fingerprint and Sclera. ECG will add liveness detection over fingerprint and Sclera. Sclera will remove age constraint, wear and tear, easy duplication over fingerprint and cataract, visually impaired issues over IRIS. Fingerprint and Sclera will give advantage over ECG in case of intra-subject variability like cardiac conditions. So fusion of these three will be a robust system. ECG and Fingerprint are natural choices as both can be captured from fingers and Sclera will add robustness against physical damage to fingers and cardiac condition.

References 1. Pinto JR, Cardoso JS, Lourenço A (2018) Evolution, current challenges, and future possibilities in ECG biometrics. IEEE Access 6:34746–34776 2. Sabhanayagam T, Venkatesan VP, Senthamaraikannan K (2018) A comprehensive survey on various biometric systems. Int J Appl Eng Res 13(5):2276–2297 3. Silva H, Lourenco A, Canento F, Fred AL, Raposo N (2013) ECG biometrics: principles and applications. In: BIOSIGNALS, pp 215–220 4. Neehal N, Karim DZ, Banik S, Anika T (2019. Runtime optimization of identification event in ECG based biometric authentication. In: 2019 international conference on electrical, computer and communication engineering (ECCE), pp 1–5 5. Hammad M, Liu Y, Wang K (2018) Multimodal biometric authentication systems using convolution neural network based on different level fusion of ECG and fingerprint. IEEE Access 7:26527–26542

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6. Agrafioti F, Gao J, Hatzinakos D, Yang J (2011) Heart biometrics: theory, methods and applications. In: Biometrics. InTech, Shanghai, China, pp 199–216 7. Jain A, Ross A (2004) Multibiometric systems, communications of the ACM, special issue on multimodal. Interfaces 47(1):34–40 8. State-of-the-Art Report on Multimodal Biometric Fusion. http://www.biosec.org/index.php 9. Moutafis P, Kakadiaris IA (2015) Rank-based score normalization for multi-biometric score fusion. In: Proceedings of the IEEE international symposium on technologies for homeland security, Waltham, MA, USA, 5–6 Nov 2015 10. Eskandari M, Toygar Ö (2015) Selection of optimized features and weights on face-iris fusion using distance images. Comput Vis Image Underst 137:63–75. [CrossRef] 11. Bouzouina Y, Hamami L (2017) Multimodal biometric: Iris and face recognition based on feature selection of Iris with GA and scores level fusion with SVM. In: Proceedings of the international conference on bio-engineering for smart technologies (BioSMART), Paris, France, 30 Aug–1 Sept 2017 12. Ammour B, Bouden T, Boubchir L (2018) Face-Iris multimodal biometric system based on hybrid level fusion. In: Proceedings of the 41st international conference on telecommunications and signal processing (TSP), Athens, Greece, 4–6 July 2018 13. Chang K, Bowyer KW, Sarkar S, Victor B (2003) Comparison and combination of ear and face images in appearance-based biometrics. IEEE Trans Pattern Anal Mach Intell 25(9):1160–1165 14. Yuan L, Mu Z-C, Xu X-N (2007) Multimodal recognition based on face and ear. In: International conference on IEEE wavelet analysis and pattern recognition, vol 3, pp 1203–1207 15. Yaman D, Eyiokur FI, Ekenel HK (2019) Multimodal age and gender classification using ear and profile face images. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW), Long Beach, CA, USA 16. Xu X, Mu Z (2007) Multimodal recognition using ear and face profile based on CCA. Appl Res Comput 24(11):312–314 in Chinese 17. Xu X, Mu Z (2007) Feature fusion method based on KCCA for ear and profile face based multimodal recognition. In: Proceedings of the 2007 IEEE international conference on automation and logistics, Jinan, China, pp 620–623 18. Theoharis T, Passalis G, Toderici G, Kakadiaris IA (2008) Unified 3D face and ear recognition using wavelets on geometry images. Pattern Recogn 41(3):796–804 19. Huang Z, Liu Y, Li C, Yang M, Chen L (2013) A robust face and ear based multimodal biometric system using sparse representation. Pattern Recogn 46(8):2156–2168 20. Xu X, Mu Z (2007) Multimodal recognition based on fusion of ear and profile face. In: Proceedings of the fourth international conference on image and graphics. Chengdu, China, pp 598–603 21. Yan P (2006) Ear biometrics in human identification. Ph.D. thesis, University of Notre Dame, Notre Dame, Indiana 22. Mahoor MH, Cadavid S, Abdel-Mottaleb M (2009) Multimodal ear and face modeling and recognition. In: Proceedings of the 16th IEEE international conference on image processing, Cairo, Egypt, pp 4137–4140 23. Islam SMS, Bennamoun M, Mian AS et al (2009) Score level fusion of ear and face local 3D features for fast and expression invariant human recognition. Image Analysis and Recognition. Springer, Berlin, Germany, pp 387–396 24. Huang Z, Liu Y, Wang X (2015) Study on sparse representation based classification for biometric verification 25. Kale VK et al (2018) A coupling of voice and iris based multimodal biometric system for person authentication. Int J Comput Sci Eng 6(11). E-ISSN: 2347-269 26. Komeili M, Armanfard N, Hatzinakos D (2018) Liveness detection and automatic template updating using fusion of ECG and fingerprint. IEEE Trans Inf Forensics Secur 13(7):1810–1822 27. Zhao CX, Wysocki T, Agrafioti F, Hatzinakos D (2012) Securing handheld devices and fingerprint readers with ECG biometrics. In: Proceedings IEEE 5th International Conference on IEEE Biometrics, Theory, Applications and Systems (BTAS), pp 150–155

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28. Manjunathswamy BE, Abhishek AM, Thriveni J, Venugopal KR, Patnaik LM (2014) Multimodel biometrics using ECG and fingerprint. In: Proceedings of the international conference on advanced communications and computer network 29. Singh YN, Singh SK, Gupta P (2012) Fusion of electrocardiogram with unobtrusive biometrics: an efficient individual authentication system. Pattern Recogn Lett 33(14):1932–1941 30. Zokaee S, Faez K (2012) Human identification based on electrocardiogram and palmprint. Int J Electr Comput Eng (IJECE) 2(2):261–266 31. Fatemian SZ (2009) A wavelet-based approach to electrocardiogram (ECG) and phonocardiogram (PCG) subject recognition. A thesis, Master of Applied Science, Graduate Department of Electrical and Computer Engineering, University of Toronto 32. Sim T, Zhang S, Janakiraman R, Kumar S (2007) Continuous verification using multimodal biometrics. IEEE Trans Pattern Anal Mach Intell 29(4):687–700 33. Boumbarov O, Velchev Y, Tonchev K, Paliy I (2011) Face and ECG based multi-modal biometric authentication, advanced biometric technologies. In: Chetty G, Yang J (eds). InTechOpen. https://doi.org/10.5772/21842 34. Jaina A, Nandakumara K, Ross A (2005) Score normalization in multimodal biometric systems. J Pattern Recogn Soc 38:2270–2285 35. Pouryayevali SH (2015) ECG biometrics: new algorithm and multimodal biometric system. A Thesis, Master of Applied Science, Department of Electrical and Computer Engineering, University of Toronto 36. Raju AS, Udayashankara V (2017) Database evaluation of ECG fingerprint and face multimodal biometric system. In: International conference on signal, image processing communication and automation (ICSIPCA), JSSATE, Bengaluru, pp 207–215. Grenze ID: 02.MHICSIPCA.2017.1.32 37. Shekhar S, Patel VM, Nasrabadi NM, Chellappa R (2014) Joint sparse representation for robust multimodal biometrics recognition. IEEE Trans Pattern Anal Mach Intell 36(1):113–126 38. Barra S, Casanova A, Fraschini M, Nappi M, Barra S, Casanova A, Fraschini M, Nappi M (2017) Fusion of physiological measures for multimodal biometric systems. Multimed Tools Appl 76(4):4835–4847 39. Sabri M, Moin MS, Razzazi F (2019) A new framework for match on card and match on host quality based multimodal biometric authentication. J Signal Process Syst 91:163–177 40. Ching HC (2013) Performance evaluation of multimodal biometric systems using fusion techniques. PhD Thesis. Faculty of Computing and Informatics. Multimedia University. Malaysia 41. Regouid M, Touahria M, Benouis M, Costen N (2019) Multimodal biometric system for ECG, ear and iris recognition based on local descriptors. Multimedia Tools Appl 78(16):22509–22535 42. Hammad M, Wang K (2019) Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network. Comput Secur 81:107–122 43. El-Rahman SA (2020) Multimodal biometric systems based on different fusion levels of ECG and fingerprint using different classifiers. Soft Comput 1–34 44. Ross AA, Nandakumar K, Jain AK (2006) Handbook of multibiometrics, 1st edn. SpringerVerlag. Number ISBN-13: 978-0-387-22296-7

Performance Analysis of Nature Inspired Optimization Based Watermarking Schemes Vijay Krishna Pallaw

and Kamred Udham Singh

Abstract In the digital era, nature is an incredible and massive source of motivation for learning hard and complex issues in computer science. Nature is a mother of learning because it shows various dynamic, powerful, complex, and exciting concepts. These algorithms are basically designed for solving various optimization problems. These algorithms always provide optimal results for the problem. In a few decades past, many researchers have introduced a large number of natureinspired algorithms. Few natures inspired algorithms are more effective and useful comparatively other optimization algorithms to provide optimal results with watermarking techniques such as SVD (Singular Value Decomposition), DWT (Discrete Wavelet Transform), and DCT (Discrete Cosine Transform). This paper presents a short description about optimal digital image watermarking algorithms and applications of nature-inspired algorithms: Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) & Firefly (FA). The primary purpose of the review is to acknowledge a comprehensive analysis of different nature-inspired algorithms based on their source of inspiration, characteristics, fundamentals, and implementations where these algorithms are properly executed to obtain the optimal solutions for a digital watermarking strategy for digital medical images. Keywords Nature inspired algorithms · SVD · DWT · DCT · PSO · ABC · FA · And watermarking

1 Introduction Due to tremendous technological developments, many digital medical images have been communicated and broadcasted with the fastest speed from one location to another worldwide. During the transmission, unauthorized persons may access and

V. K. Pallaw (B) · K. U. Singh School of Computing, Graphic Era Hill University, Dehradun, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_12

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illegally modify medical images. That’s why security is required. Digital watermarking is the best technique that secures medical images. By using a digital watermarking strategy, medical image security may be improved. Digital watermarking is a technique to insert a watermark, such as an image, text, or other digital information, without changing its display quality. Digital watermarking techniques conform authenticity, & security of medical images or other applications, according to ref. [1, 2], digital watermarking techniques have the following components: a watermark inserter, transmission media, and a watermark separator. The basic process in watermarking technique is that a watermark inserter inserts a digital watermark into the initial medical image; after that watermark, the extractor removes the watermark from watermarked image without assaults. The basic needs of watermarking images in digital forms [3, 4]: Robustness: Watermarking approach must be secured original data against any modification. Imperceptibility: No changes in the medical image’s display quality after inserting the watermark. That is, the visual quality of both watermarked and original images should be similar. Embedding Capacity: The watermarking approach must be allowed to hide the enormous size of the watermark [2]. The watermarking technique classified the domains into spatial and transformed domains [5, 6]. The spatial domain technique is simple to use but gives less imperceptibility because the pixels of the host image can be easily changed. The spatial domain provides weak robustness against assaults like noise and filtering, which can be destroyed effortlessly by deformation. The transform domain is also called the frequency domain. Since the transform domain provides more robustness and imperceptibility than the spatial domain, transform domain strategies are utilized more in the digital watermarking process. There are various transform domain methods, such as DFT, DFT, SVD, and DCT. In the digital era, nature is an excellent source of learning and inspiration to build intelligent systems and provides solutions to complicated issues. Nature is a great educator with tremendous inspiration abilities, and research scholars are attempting to implement nature into technology. Now a day, many NIAs have excellent potential to evaluate complex engineering optimization problems such as PSO, ACO, and ABC. Nature-inspired algorithms have emerged as powerful optimization algorithms to resolve complicated issues. In applying digital medical image watermarking schemes, nature-inspired algorithms help find the optimal solutions to complex problems and preferable parameters for watermark insertion.

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2 Nature-Inspired Algorithms (NIAs) for Optimal Watermarking In applying digital medical image watermarking optimization, NIAs help to enhance the execution of digital medical image watermarking strategies. This Section presents a brief overview of nature-inspired algorithms and shows extensive analysis of the most valuable works in medical image watermarking strategies for optimization.

2.1 Particle Swarm Optimization James Kennedy & Russel C. Eberhart proposed Particle Swarm Optimization (PSO) in 1995. The word Swarm intends the bird flocking or fish schooling, and the word Particle denotes a bird in a flock or a fish in a school. That is, the concept of PSO was influenced by the communal conduct of bird flocking or fish schooling. This contains synchronic movement and uncertain and periodic changes of direction. The cooperative behaviors of the birds organize a journey without collision in the search space toward the nest. Therefore, every Particle in Swarm maintains its speed and proper position with the closest Particle. An arbitrary variable has been included in speed to keep up consistent and uniform directions. The Swarm is made of particles that have proper position and speed. The particles of Swarm have the following essential abilities: their knowledge, the best location, and good information about global or their neighbors. In a swarm, the individuals communicate their best positions to each other and maintain their inter-individual distance and speed. Each member maintains their own flying with other members by their past experience. In a swarm, every Particle has the following valuable knowledge to create an appropriate modification in its location and speed [7–9]. • A global best position (gbest). • The individual best position (pbest), is the individual’s best solution. In the computer simulation of particle swarm optimization (PSO), every member has their speed and position. Every member thinks of the individual pbest and shares the gbest among other members to get best solution. The new position of the Particle depends on the distance of its present location from pbest and gbest. Equations 1 and 2 can be evaluated for the new velocity and new position of the particles. new_ velocity = current_ velocity + c1 r1 (pbest − current_ position) + c2 r2 (1) new_ position = current_ position + new_v elocity

(2)

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Fig. 1 PSO algorithm

where r1 & r2 are two arbitrary variables that exist between 0 and 1. c1 & c2 are the learning factors (Fig. 1). In ref. [10], The author introduced a robust watermarking strategy to pravent the medical data using DWT, DCT, and SVD transform. Using PSO & HVS strategies, evaluate the optimal value of Normalized cross-correlation (NCC) of medical image. In ref. [11], the author uses SVD & DWT along with PSO techniques to enhance the functioning of watermarking for region of interest (ROI), but it is the time taken. In ref. [12], The author uses SVD & DWT to improve watermarking performance and obtain optimal solution multi-objective POS (MOPSO) strategy. In ref. [13], the author proposed a watermarking scheme to provide better robustness of watermarking by using DWT and SVD along with a dynamic PSO algorithm. In ref. [14], the author introduced a fusion watermarking strategy by utilizing DCT, SVD, LWT, and DFAT along with the PSO algorithm to achieve better imperceptibility, robustness, and secure watermarking. In ref. [15], author a watermarking scheme to achieve more robustness & imperceptibility by using DWT, SVD, and guided dynamic PSO algorithm. The guided dynamic PSO (GDPSO) algorithm achieves better performance. In ref. [16], the author proposed robust watermarking with the help of the PSO method to prevent medical image copyright, especially color images. In ref. [17–19], authors have used the PSO algorithm to optimize the different scaling factors of medical image watermarking (Table 1).

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Table 1 Summary of different watermarking methods with PSO algorithm Ref. No

Watermarking techniques, along with the PSO algorithm

Achieved the objective

[10]

SVD & DCT with SVD provides a better human vision of WQPSO watermarked images by using WQPSO

The human visual system (HVS) provides a difficult calculation

[11]

SVD & DWT

Enhanced the watermarking performance with the help of ROI

Time taken process

[12]

DWT, SVD, and multi-objective PSO

Improves the performance of watermarking

Due SVD technique, a false-positive problem is associated

[13]

Dynamic PSO, DWT& SVD

Provides better robustness of watermarking

Due SVD technique, a false-positive problem is associated

[14]

DFAT, LWT, SVD An encrypted watermark image resolves SVD No work to & DCT false-positive problem. For better performance, improve the apply a fusion of different watermarking complexity techniques

[15]

DWT, SVD, and GDPSO

Provides better robustness & imperceptibility of watermarking. Using the guided dynamic PSO(GDPSO) algorithm, achieves better performance

Limitation

Time complexity is an issue

Other latest applications of PSO are data clustering [20], resource allocation in the cloud [30], inventory and location control in supply chain networks [31], online dictionary learning [32], and vehicle routing problems [33]. The recent application of PSO is combined with grammatical evaluation to create a hybrid optimization concept called "grammatical swarms."

2.2 Artificial Bee Colony (ABC) Karaboga innovated the ABC algorithm in 2005 [21], which was used for numerical problem optimization. The smart foraging behavior of bees inspires this algorithm. This algorithm basically depends on the concept of managing and partitioning work. A bee can get either positive or negative feedback. A honey bee searching for a good food source will get positive feedback, while a honey bee searching for a poor source of food will get negative feedback. This algorithm consists of the following

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components: employed foragers bee, unemployed foragers bee, and food source. The employed and unemployed bees are searching a good source of food surrounding the hive. After finding the rich food source of any bee, it communicates the information to the remaining bees, and the remaining bees follow the same food source. In ABC algorithm [22], the following categories of bees exist: employed bees, onlooker bees, and scout bees. All these bees have different activities in the hive. The scout bees are finding the source of food randomly surrounding the colony. Once scout bees search, the food source becomes employed bees. Employed bees locate their food source, calculate their amount of honey, return to the colony, and attract other bees’ attention. After attracting the attention of other bees, she starts one kind of dance near to hive called waggle dance or round dance. The dancing bee transmits the following information to the rest of the bees: The direction in which the food source is found, the distance of food source, and the quality rating (fitness). The transmission of information depends on the quality of richness of the food source. The quality of the food source depends on the dance duration of bees. During the search cycle, the following steps have been followed by bees [21, 23] i. Initially, the employed bee locates the rich food source and evaluates the quantity of honey. ii. After that, employed bees transmit the information of honey quantity among onlooker bees after receiving the information that onlooker bees have chosen a rich food source. iii. In the third step, scout bees discover the search region for new food sources. In the simulation of an ABC, the rich food source represented an achievable result of a problem. Every employed bee has been related to each food source. The quantity of nectar represents the fitness of the objective function. A search for a rich food source by onlooker bees is based on the expectation of the food source. The scout bee finds out the new source of food without any instruction. In Artificial Bee Colony, employed bee search the new source of food with the help of surrounding food sources, and the profitability of new sources has been evaluated with the help of these profitabilities; onlooker bees calculate the distance of the newly founded source of food. A greedy approach is applicable between the selection of new food sources and current food sources. Finally, the scout bees recognized the rejected food source and replaced it with an arbitrary one. If consecutive repetitions don’t enhance the profitability of a food source, then it will be rejected food source. Equations 3 and 4 can calculate the new position and profitability, respectively new_ position = current_ position + R (current_ position _ k)

(3)

where R is a random number and -1 ≤ R ≤ 1. k is a random dimension index, and k lies between 1 to n. profitability = fitness/ total_ fitness The control parameters of the ABC algorithm are:

(4)

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• Employed bee’s number, because each employed bee is concerned with each source of food (solutions). • The maximum number of cycles. • The value of limit [23]. This algorithm is a relatively simple, flexible, and robust method. In ref. [24], the author proposed robust watermarking using SVD and wavelet techniques along with the ABC algorithm. In ref. [25], the author concentrates on watermarking strategy’s robustness, security, and capacity by utilizing Arnold encryption and the ABC algorithm. In ref. [26], the author proposed robust watermarking by using DCT with the ABC algorithm. The author’s primary purpose is to provide only the robustness of watermarked image but not concentrate on the imperceptibility of the image. In ref. [27], the author proposed robust watermarking using the SVD technique, and IWT is used to remove false-positive problems which are arisen due to the SVD technique & by using the ABC algorithm to optimize the entire procedure. In ref. [28]. The author focused on removing the false-positive problem and improving the imperceptibility by using DWT and SVD watermarking techniques and the ABC algorithm to optimize the entire procedure. In ref. [29], the author proposed a robust watermarking strategy using DWT for high dynamic range images, and the ABC algorithm is used for optimization (Table 2). Table 2 Summary of different watermarking methods with the ABC algorithm Ref. No

Watermarking techniques, along with the ABC algorithm

Achieved the objective

Limitation

[24]

SVD and wavelet transform

It gives the best quality for the watermarked image

The SVD technique gives false-positive results

[25]

SLT and Arnold encryption

Provides better robustness, security, and capacity for lossless watermarking

Pending the work to make efficient

[26]

DCT

Improve the robustness with limited Not focus on the distortion of the image without focusing on imperceptibility imperceptibility of image

[27]

SVD & IWT

Improve both robustness and security. Integer wavelets transform (IWT) removes false-positive issues due to SVD

[28]

SVD & DWT

Improve the watermarking imperceptibility Not concentrated on robustness

[29]

DWT

Improve both robustness and imperceptibility

Pending the work to make efficient

Not concentrated on the security of HDRI images

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2.3 Firefly Algorithm (FA) Firefly algorithm is a meta-heuristic algorithm, which is introduced by Xin-She Yang in 2008. It is flashing-based algorithm in which every firefly has unique flashing pattern. Each firefly is unisexual and attraction of each firefly depends on the intensity of flashing. This algorithm is used meta-heuristic strategy to obtain the optimal results of the issues and is performed the searching in two ways: from left to right and from top to bottom. The firefly algorithm follows the following rules [39] • Each firefly is unisexual and attracts to each other for sexual activities by using their flashing. • Each firefly’s attraction is directly proportional to their intensity of flashing. • The fitness of objective function is equal to the intensity of flashing. In ref. [34], author proposed robust and secure watermarking scheme by using SVD, DCT, and DWT watermarking techniques. For optimal solution used chaotic firefly algorithm. In ref. [35], author resolved the false-positive problem by using LWT, which is associated with SVD. For optimal results used firefly algorithm along with bat algorithm. In ref. [36], author proposed watermarking scheme by using LWT along with regression tree. Firefly algorithm is used to get optimal results. In ref. [37], author introduced watermarking techniques to improve imperceptibility and robustness by using DWT along with SVD techniques, for better results used ODFA algorithm. In ref. [38], author is used DDFA algorithm along with Hadamard transform to enhance the robust of watermarking (Table 3). Table 3 Summary of different watermarking methods with the Firefly algorithm Ref. No

Watermarking Achieved the objective techniques along with Firefly algorithm

Limitation

[34]

SVD, DWT, & DCT along with Chaotic firefly algorithm

Chaotic firefly algorithm enhanced the performance of watermarking scheme

Proved poor visual quality in LSB as compared to JPEG

[35]

SVD, & LWT along with bat algorithm

It is useful for color images. Provides optimal results by using with a combination of bat & firefly algorithms

There is no focus on time complexity

[36]

Regression tree along with LWT

Enhance the imperceptibility and robustness of watermarking image

There is no focus on time complexity

[37]

DWT, & SVD along with ODFA

Provides more robustness against different attacks

There is no focus on secure watermark image

[38]

DDFA along with Hadamard transform

Provides more robustness & Increase the space imperceptibility of watermarking scheme complexity

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3 Conclusion In the digital era, security, authenticity & reliability are significant issues for medical images when transferred from one location to another worldwide through transmission media. The digital watermarking techniques are suitable, but each technique has its limitations. However, nature-inspired algorithms are beneficial to provide the optimal solution. Now a day, PSO and ABC algorithms have been used frequently by many research scholars to optimize digital watermarking techniques. The primary aim of this paper is to analyze the performance of watermarking techniques along with nature-inspired algorithms and inspire other research scholars to develop suitable and efficient algorithms to solve large-scale digital watermarking-related problems. Compared to other nature-inspired algorithms, the firefly algorithm provides better results with watermarking techniques. Therefore, many research scholars attract to the firefly algorithm.

References 1. Kumar L, Singh KU (2022) Color ultrasound image watermarking scheme using FRT and hessenberg decomposition for telemedicine applications. JUCS—Journal Univers Comput Sci 28(9):882–897 2. Singh, Kamred Udham, et al. (2022) Image-based decision making for reliable and proper diagnosing in NIFTI format using watermarking. Multimed Tools Appl 1–27 3. Kumar, Abhishek, et al. (2022) Robust watermarking scheme for nifti medical images.“ Computers, Mater Contin 71.2:3107–3125 4. Lalit Kumar Saini, Vishal Shrivastava (2014) A survey of digital watermarking techniques and its applications. Int J Comput Sci Trends Technol (IJCST)—Volume 2 Issue 3 5. Singh KU, Singh VK, Singhal A (2018) Color image watermarking scheme based on QR factorization and DWT with compatibility analysis on different wavelet filters. J Adv Res Dyn Control Syst 10(06):1796–1811 6. Singh, Kamred Udham, et al. (2022) Secure watermarking scheme for color DICOM images in telemedicine applications. Comput Mater & Contin 70.2: 2525–2542 7. James Kennedy and Russell Eberhart (1995) Particle swarm optimization. In Int Conf Neural Netw. 1942–1948 8. Kennedy J, Eberhart RC (2001) Swarm Intelligence. Morgan Kaufmann 9. Engelbrecht AP (2005) Fundamentals of computational swarm intelligence, Wiley 10. Soliman MM, Hassanien AE, Onsi HM: An adaptive watermarking approach based on weighted quantum particle swarm optimization. The natural computing applications forum, pp 1–13 (2015). 11. Shih FY, Zhong X, Chang I-C, Satoh S: An adjustable-purpose image watermarking technique by particle swarm optimization. Multimedia tools application. Springer, pp 1–20 (2017) 12. Saxena N, Mishra KK: Improved multi-objective particle swarm optimization algorithm for optimizing watermark strength in color image watermarking. Springer, pp 1–20 (2017) 13. Saxena N, Mishra KK, Tripathi: A DWT-SVD-based color image watermarking using dynamicPSO. Advances in intelligent systems and computing, pp 343–351 (2018). 14. Zhou NA, Luo AW, Zou WP: Secure and robust watermark scheme based on multiple transforms and particle swarm optimization algorithm. Multimedia tools application. Springer, pp 1–17 (2018).

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15. Zheng Z, Saxena N, Mishra KK, Kumar Sangaiah: A Guided dynamic particle swarm optimization for optimizing digital image watermarking in industry applications. Fut Gener Comput Syst, pp 1–46 (2018) 16. O. Fındık ˙I, Babao˘glu, Ülker E (2010) A color image watermarking scheme based on hybrid classification method Particle swarm optimization and k-nearest neighbor algorithm, Optics Communications, 283(24), pp 4916–4922 17. V. Aslantas AL, Dogan, Ozturk S (2008) DWT-SVD based image watermarking using Particle Swarm Optimizer. In: IEEE International Conference on Multimedia and Expo, Hannover, Germany, June 23—April 26, pp. 241–244 18. Rao VSV, Shekhawat RS, Srivastava VK (2012) A DWT-DCT-SVD based digital image watermarking scheme using particle swarm optimization In: IEEE Students’ Conference on Electrical, Electronics and Computer Science, Bhopal, India, March 1–2, pp 1–4 19. Run R-S, Horng S-J, Lai J-L, Kao T-W, Chen R-J (2012) An improved SVD-based watermarking technique for copyright protection. Expert Syst Appl 39(1):673–689 20. Ahmed AA. Esmin, Rodrigo A Coelho, Stan Matwin (2015) A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data. Artif. Intell. Rev. 44, 23–45 1 21. Dervis Karaboga (2005) An idea based on honey bee swarm for numerical optimization 22. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132 23. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471 24. Ali M, Ahn CW, Pant M, Siarry P (2014) An image watermarking scheme in wavelet domain with optimized compensation of singular value decomposition via artificial bee colony. Information Sciences Elsevier, pp 1–20 25. Ansari IA, Pant M, Ahn CW (2016) Artificial bee colony optimized robust-reversible image watermarking. Multimedia tools applications. Springer, pp 1–25 26. Abdelhakim AM, Saleh HI, Nassar AM (2016) A quality guaranteed robust image watermarking optimization with artificial bee colony. Expert systems with applications. Elsevier, pp 1–10 27. Ansari IA, Pant M, Ahn CW (2016) Robust and false positive free watermarking in IWT domain using SVD and ABC. Eng Appl Artif Intell. Elsevier, pp 114–125 28. Ansari IA, Pant M (2016) Quality assured and optimized image watermarking using artificial bee colony. Int J Syst Assur Eng Manag, pp 1–13 29. Yazdan Bakhsh F, Moghaddam ME (2018) A robust HDR images watermarking method using artificial bee colony algorithm. J Inf Secur Appl 41:12–27 30. Mohana RS (2015) A position balanced parallel particle swarm optimization method for resource allocation in cloud. Indian J Sci Technol 8(S3):182–188 31. Mousavi SM, Bahreininejad A, Musa SN, Yusof F (2017) A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network. J Intell Manuf 28(1):191–206 32. Wang L et al (2015) Particle swarm optimization based dictionary learning for remote sensing big data. Knowledge-Based Syst 79:43–50 33. Baozhen Yao, Bin Yu, Ping Hu, Junjie Gao, Mingheng Zhang (2016) An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot. Ann. Oper. Res. 242, 2, 303–320 34. Dong H, He M, Qiu M (2015) Optimized gray-scale image watermarking algorithm based on DWT DCT-SVD and chaotic firefly algorithm. In: IEEE International Conference on CyberEnabled Distributed Computing and Knowledge Discovery, pp 310–313 35. Sejpal S, Shah N (2016) A novel multiple objective optimized color watermarking scheme based on LWT-SVD domain using nature based bat algorithm and firefly algorithm. In: IEEE international conference on advances in electronics, communication and computer technology (ICAECCT) India, pp 38–44

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Evolutionary Data Engineering Applications

A Review of Ensemble Methods Used in AI Applications Priyanka Gupta, Abhay Pratap Singh, and Virendra Kumar

Abstract This study focuses to examine the employ of ensemble methods for improving generalization performance which is the most important in the recent study of machine learning. Ensemble learning combines numerous models and deep learning (DL) models with multi-layered ones and also outperforms deep or classic classification models in terms of performance. Deep ensemble learning methods combine the merits of both ensemble learning and the DL models so that the overall result has better performance. This review analyzes current deep ensemble models and provides a comprehensive overview as Bagging, boosting, and stacking are examples of ensemble models. The negative correlation-based deep ensemble models are explicit or implicit ensembles, homogeneous or heterogeneous ensembles, decision fusion strategies, un-supervised, semi-supervised, reinforcement learning, internet, and multilevel featured ensemble models. Deep ensemble models are used in many fields. The author reviews the recent advancement in ensemble deep learning methods and formulates the research objective at the end of this work and future recommendations. Keywords Ensemble learning · Bagging · Boosting · Deep learning

1 Introduction Ensemble learning combines numerous models and deep learning models with multilayered also outperforms deep or classic classification models in terms of performance. Deep ensembles learning methods combine the merits of both ensemble P. Gupta (B) Suresh Gyan Vihar University, Jaipur, Rajasthan, India e-mail: [email protected] A. Pratap Singh G L Bajaj Institute of Technology and Management, Mathura, India V. Kumar G L Bajaj Institute of Technology and Management, Greater Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_13

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learning and DL models so that the overall result has better performance. The categorizing of novel observations that is relied on hypothesis H obtained from collection of trained data is a classification problem. Input data set mapping characteristics to acceptable target classes is represented by hypothesis H. The basic aim of learning hypothesis H is to get as close to the actual unknown function as possible to minimize the generalized error. These approaches have many applications, from medical diagnosis to remotely sensed. Various classification methods are unsupervised, supervised, few-shot, and one-shot classification. Only unsupervised or supervised classification problems are discussed in this paper. Hypothesis H is built supervised in supervised learning based on recognized output tags given in learning data collections, whereas hypothesis H is formed unsupervised in unsupervised learning because no recognized output values are obtainable with training data. That method, also familiar as clustered, develops hypothesis H relied on training data set similar and dissimilar. In general, the main aim of developing hypothesis H in the ML field is to perform better while applied to unidentified data. Marquis de Condorcet suggested a formula to show that if each voter is correct the probability greater than 0.5 and voters are independent, then adding further voters enhances the probability of the majority voting becoming accurate awaiting those approaches [1]. While Marquis de Condorcet established that theorem in the field of political science but had no knowledge of ML, it is same process that conducts to improve ensemble quality of the model. The Marquis de Condorcet theorem’s assumptions also apply to ensembles. Statistical and computational are some of the reasons behind ensemble learning’s performance. DL automates the taking out of high-quality features in the phase of AI using a hierarchical feature learning method in which the higher layer of features is created on preceding bunch of layers. While Image Net Large Scale Recognition Challenge (ILSVRC) contests, DL has been effectively utilized across a variety of domains and has achieved state of art. It has shown promise in a variety of domains, as well as object detection, semantic segmentation, edge detection, and others [2]. Apart from this, the deep ensemble learning algorithm is a difficult undertaking due to the high computation complexity. Different perspectives have been offered to comprehend how DL methods learn characteristics such as learning through a hierarchy of concepts via several layers of representations. Considering the benefits of deep networks from complex architectures, there are many slowdowns such as vanishing or exploding gradients and deterioration issues which prevent achieving this stage. Recently deep networks have become viable through the Major highways and Residual networks. Together these networks made it possible to train extremely deep networks. Several surveys exist in previous studies that emphasize primarily on the study of ensemble learning, such as learning ensemble models in classified problems, regression problems, and clustering. Despite the fact that provided some insight into deep ensemble methods, it was unable to provide a full assessment of deep ensemble methods, whereas studied ensemble deep models provide a complete overview of deep ensemble models [3].

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2 Related Work The following subheadings can be used to discuss the numerous causes behind ensemble learning’s success.

2.1 Bias-Variance Decomposition Primarily, the effectiveness of ensemble approaches in regression issues was studied theoretically. The correct ensemble classifier assures a lesser squared error than the individual predictors of the classifier using ambiguity decomposition. For only dataset-based ensemble approaches, ambiguity decomposition was given later, multi-dataset bias-variance–covariance decomposition. Many theories, such as biasvariance strong correlation, stochastic discriminating, and margin theory, have supported ensemble methods. Due to the categorical character, the mentioned decomposition error equations cannot be straightforwardly used for datasets with discrete class labels [4]. For producing ensemble methods, many approaches such as bagging and boosting have been presented. Bagging lowers variation between base classifiers, whereas boosting-based ensembles minimize bias and variance [5].

2.2 Diversity Enhancing the diversity among base classifiers is one of the primary reasons for success of ensemble approaches. To create diverse classifiers, many approaches have been used. Various methodologies such as bootstrap aggregation (bagging, Adaptive Boosting (AdaBoost), random subspace, and RF ) are used to generate many datasets from similar dataset to train distinct predictors with diverse outcomes. Numerous outputs have been established besides several datasets for the regulation of the base learners in an attempt to promote variation in the output data. One example is output smearing, which uses random noise to bring in variety in the output space [6].

3 Ensemble Strategies Many ensemble procedures have changed over time, resulting in improved generalization of learning models. There are some following broad categories of ensemble strategies.

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3.1 Bagging It is called as bootstrap aggregation, is a common tool for obtaining ensemble-based methods and is used to improve the performance of ensemble classifiers. Bagging’s major goal is to create a set of free samples that are similar in size and distribution as the authentic data. Create an ensemble predictor from the series of observations that is better than the single predictor derived from the original data. In the original models, bagging adds two steps: First, creating bagging data and passing each bag of samples to the base models, second a mechanism for merging several predictors’ predictions. Samples for bagging can be created with or without substitution. The outputs of the base determinants can be united in a diversity of ways, with majority voting being used for classification problems and averaging being utilized for regression problems. Random forest is a more advanced variant of decision trees that employs a bagging approach to improve the predictions of the basis classifier that is a decision tree. In RF, just a subclass of features is arbitrarily picked and used for splitting at each tree split. The goal of this strategy is to avoid overfitting by decorrelating the trees. The authors indicated heuristically that variance of bagged predictor is lower than that of the original predictor, and they claimed that bagging is preferable in higher dimensional information. Bagging, on the other hand, does not depend on the data dimensionality, according to an investigation of the smoothing impact of bagging [7, 8]. A theoretical model of how bagging produces smooth hard judgments with low variance and mean squared error may be found. It is computationally efficient; half sub bagging is as precise as bagging. Bagging has been combined with various machine learning methods in several efforts. This method is used in [9] to produce numerous bags of the dataset, and each bag is used to train multiple support vector machines individually. Majority voting, least squares estimation weighting, and a two-layer hierarchical technique are used to integrate the models’ output. Another support vector machine (SVM) is employed in the double-layer hierarchical approach to efficiently aggregate the results of the numerous SVMs. Tao et al. [10] employed an asymmetric bagging method to create an ensemble model to deal with class imbalance issues. A comparison of bagging, boosting, and basic ensembles [11] indicated that boosting outperforms bagging and basic ensembles at larger sample rejection rates. The difference between the boosting, bagging, and basic ensembles evaporates as dismissal rate rises. Bagging-based multilayer perceptron [12] used bagging to train numerous perceptron with matching bags, demonstrating that bagging-based ensemble models outperform individual multilayer perceptron. Bagging regularized neural networks and hence provided improved generalization, according to an examination of the bagging strategy and other regularization strategies. An ensemble of bagging with neural networks for predicting short-term load forecasts [6]. For the class imbalance concerns, Neighborhood Balanced Bagging [5] used neighborhood information to generate bagging samples. The authors determined

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that applying traditional diversification to the final classification approaches is more successful. Though an ensemble may upsurge computational complexity, it has the advantage of being parallelizable and can result in a significant reduction in training time, provided that technology for running parallel models is available. Because deep learning models take a long time to train, optimizing several deep models on various training bags isn’t an option. The intrusion detection system is implemented using the Ensemble Bagging approach with REP Tree as the foundation class. To increase the accuracy of classification and lower the false positive rate, appropriate characteristics from the NSL KDD dataset are chosen. The suggested ensemble method’s performance is measured in terms of classifier, model construction time, and False Positives. The Bagging ensemble using the REP Tree base class has the best accuracy of classification, according to the results. The Bagging method has the advantage of taking less time to construct the model. When compared to existing machine learning techniques, the suggested ensemble method has a low false positive rate (Fig. 1) [13].

Fig. 1 Ensemble procedure [13]

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3.2 Boosting In ensemble models, the boosting strategy is used to convert a weak learning model into a learning model with improved generalization. When linked to a single weak learner, strategies such as majority voting in classification issues or a linear mixture of weak learners in regression issues produce superior predictions. Different domains have employed boosting methods such as AdaBoost and Gradient Boosting. AdaBoost employs a greedy approach to minimize a convex surrogate function upper bounded through miscalculated loss by augmenting the existing model with the properly weighted predictor at each iteration. At each level of the learning process, AdaBoost uses the improperly categorized sample to learn an effective ensemble classifier. While AdaBoost reduces the exponential loss function, gradient boosting extends paradigm of any differential error function [14]. DB is an ensemble method that increases generalization performance by using deep decision trees or by combining them with an additional rich family classifier. The decision of which classifiers to add and what weights to choose at each level of deep boosting is based on data-dependent complication of the classifiers to that it goes. At each stage of the learning, the DB classifier is interpreted using the structural risk mitigation concept. For face expression identification, the boosted deep belief network (DBN) united the boosting technique and numerous DBNs through an objective function, resulting in a strong classification. The model iteratively acquires complicated representations to generate a good classifier [15]. Multiclass DB applied the DB method to multiclass issues, yielding theoretical, computational, and empirical findings. Boosting CNN may extra fit data due to the limited training data in every mini-batch. To minimize overfitting, the incremental Boosting CNN (IBCNN) gathered information from successive batches of training data samples. In every mini-batch, IBCNN utilizes decision stumps on the top of lone neurons as weak learners and learns weights using the AdaBoost approach. Unlike DBN, which trains weak classifiers using an image patch, incremental Boosting CNN learns weak classifiers using a fully connected layer, which means the entire picture utilized [15]. The weaker learner’s features are integrated with the global loss function to create the IBCNN model more proficient. Boosting was utilized to train the deep CNN in Boosted CNN. To include boosting weights into CNN, the least squares objective function was utilized instead of the average. Within their boosting framework, the authors also demonstrated that CNN may be exchanged through network structure to boost the effectiveness of the base classifier, because boosting upsurges the complexity of the training phase, the idea of dense connections was developed in a deep boosting architecture to solve the vanishing gradient problem for picture denoising [16]. Convolutional channel properties [16] used CNN to produce high-level data, which were then classified using the boosted forest. Because CNN contains a higher

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number of hyperparameters than boosted forest, the model showed to be more proficient in terms of efficiency and time than CNN end-to-end learning. It is used in edge detection, item proposal development, pedestrian, and facial recognition was demonstrated by the authors. Within the offline paradigmatic boosting framework, a stepwise boosting deep CNN [17] trains numerous models of CNNs. Deep progressive boosting [18] employed a transfer learning strategy to decrease the warm-up phase of training that teaches classifier from scratch when using deep boosting. During the network training, this method took use of the primary warm-up period of every increasing base model of ensemble. Snapshot boosting [19] joined the benefits of snapshot ensembling and boosting to increase generalization without raising the expense of training for boosting-based ensembles. To associate the output of base learners more effectively, it trains each base network and then joins the outputs through meta learner. According to literature, the boosting notion lies at the heart of recognized architectures such as Deep Residual networks [20, 21] and AdaNet [22]. In the context of boosting theory [23], the theoretical underpinning for the success of DeepResNet [20] was described. The authors demonstrated that the top layer’s output is a layer-by-layer boosting mechanism. Authors presented the BoostResNet multi-channel telescoping sum boosting learning framework, in which every channel is a scalar value updated through boosting rounds to reduce multi-class error rate. The primary distinction between AdaNet and Boost Resnet is that the former maps feature vectors to classifier space and boosts weak classifiers, whilst the latter uses multi-channel representation boosting. With respect to computation time, BoostResNet is more effective than Deep Resnet the authors demonstrated. In [24], the theory of boosting was protracted to online boosting and theoretical convergence guarantees were presented. Online boosting improves batch convergence guarantees were presented. In [24], the bagging and boosting ensembles were examined. The research looked at the various algorithms that relied on the bagging and boosting concepts, as well as the availability of software applications. The study focused on the practical difficulties and potential for its implementation. This research proposed an imbalanced sentiment classification system that mixtures unstable classification and ensemble learning methods. To increase the classification performance of the unstable sentiment data set, both the algorithm and the data set are utilized. This hybrid approach processes the data set using three different methods: under-sampling, bootstrap resampling, and random feature selection, all within the context of ensemble learning. Experiments on imbalanced data sets show that this ensemble strategy can improve unbalanced sentiment data set categorization efficiency.

3.3 Stacking It can be completed by merging the outputs of numerous base models in some way or by selecting the “best” base model using some approach. Stacked is an integration

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Fig. 2 Stacking method [25]

strategy in that output of regular models is combined using a meta-learning model. It is sometimes called as “model blending” or simply “blending” when the final judgment element is a linear model. In [25] was the first to introduce the concept of stacked regression. Dataset is arbitrarily divided into J equal sections using this method. Single set is utilized for checking and the rest is utilized for training in the Jth-fold cross-validation. We acquire the estimates of diverse learning models using these training testing couple divisions. The ultimate estimation is done by the meta-model which is also known as the winner-take-all method. It is a technique for decreasing biasness (Fig. 2). In classification tasks, CNN models (CNN) are commonly utilized, and the stacking method is also important. To improve the performance of the evolved block, stack it numerous times. Offers a deep hierarchical multi-patch network for image recognition in Neural Architecture Search problem. They can achieve a better outcome with the layered method of deblurring [26, 27].

3.4 Deep Ensemble Methods Use Negative Correlation The approach of negative correlation learning is useful for training learning algorithms. NCL’s major goal is to promote individual variety. Ensemble models to learn the various elements of the training data NCL reduces the practical uncertainty. Individual error functions are minimized to reduce the risk function of ensemble models. The NCL network was tested for regression and classification tasks. The assessment on categorization, different measures such as easy averaging and winner-take-all procedures were utilized. ConvNet, or decor-related convolutional networks, was proposed as a negative correlation learning architecture for crowd calculating. Counting is done using a

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pool of convolutional feature map weak regressors and regression-based ensemble learning. The major goal is to incorporate the concept of NCL into abysmal architectures [28].

3.5 Explicit or Implicit Ensembles The training of several neural networks may result in a significant rise in processing cost, making the assembly of deep neural networks a difficult alternative. Training deep networks on high-performance hardware with GPU speeding up could take weeks or months. It achieves the seemingly paradoxical goal of training a single model in such a way that it behaves like an ensemble of numerous neural networks without suffering extra expense at the lowest possible cost. An ensemble’s training time is the same as a single model’s training time in this case. The model parameters are spited in implicit ensembles, and the lone un-thinned network during trial times resembles the model average. During the training of the network, Dropout [29] constructs an ensemble network through randomly reducing hidden nodes. All nodes are active during the testing period. Dropout prevents overfitting by regularizing the network and introducing sparsity. The vector of output Overfitting is reduced since an exponential amount of models are trained with common data. During testing, weights and an implicit ensemble of networks are provided. Deleting units at random avoids unit coadaptation by making the presence of a certain unit unreliable. When compared to a conventional network, the network with dropout proceeds 2 to 3 times lengthier to train. Network of neurons as a result, a proper balance must be struck between the amount of time spent training and the time spent resting. Because the lower-level characteristics across the models are likely to be the same, explicit/implicit produces ensembles from a single network at the expense of base model diversity [25]. Various neural network initializations lead to different local minima, which is why the [30] proposed a deep ensemble model with a fully convolutional neural network ensemble. Better segmentation was achieved by using a network over multi-loss module with a coarse good recompense module. A lesion of vital serous chorioretinopathy Different types of neural networks multiple loss functions and initializations resulted in greater ensemble variability.

3.6 Homogeneous or Heterogeneous Ensembles This instruction entails instructing a cluster of basic learners from similar or distinct families. As a result, every ensemble model must be as different as feasible, and

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every base model must outperform the random guess. The similar base learner is employed several time to create an ensemble in homogeneous ensembles. Base classifiers’ families the most important thing is to train every base model therefore ensemble model is as dissimilar as probable, meaning that no two models make a similar mistake. A specific data model is the two mainly frequent methods for creating randomness in requirements and internal either a taste of the training or an ensemble. For adding variety in the amalgamation of decision trees, some ensemble models, such as Random forest [5], used both of these strategies. In neural networks, diversity is created by training models independently with distinct initializations. DL models, on the other hand, have substantial training costs, so training numerous deep neural networks is not an option. Several attempts have been attempted to obtain deep model ensembles without independent training, such as horizontal perpendicular choice of deep ensembles [6]. Multiple models are trained with varying input augmentation, regularization, and training epochs using temporal assembling. Regardless of these models, assembling several DL models is difficult because billions of parameters must be enhanced. As a result, several studies have combined DL with classical models to create different ensemble models that have reduced calculation and superior diversity. In a heterogeneous ensemble, extreme gradient boosting, deep neural networks, and logistic regression are combined for default prediction [5]. For text classification used heterogeneous ensemble that combined, MVNB, MNB, SVM, RF, and CNN [31, 32]. A heterogeneous base classifier suggested [33] on biomedical image classification which brain disease on the dataset of biomedical image with accuracy for brain image is 97.62% and for chest X-ray is 95.24. Authors discussed in [34] Alzheimer’s disease for three MRI and one fMRI public datasets using Deep ensemble strategy with an accuracy of 98.51% in the binary case, and 98.67% in the multiclass case. Similarly, in [35] discussed medical image of breast cancer for breast cancer histology image dataset using Multi-level Context and Uncertainty aware method with an accuracy 98.11%.

4 Application of Ensemble Learning Methods In this section, we summarized several ensemble learning methods utilized in diverse areas such as DL and ML-based applications as demonstrated in Table 1.

5 Conclusion and Future Scope This paper discussed the current advancement in ensemble DL methods. To comprehend ensemble learning’s success, the theoretical underpinning of ensemble learning has been explored. The review also showed that AI-based application that utilizes ensemble methods achieved more than 90% accuracy. Deep ensemble models have

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Table 1 Summary of reviewed applications utilized in ensemble learning methods Dataset

Accuracy

2022 Four heterogeneous base classifiers Bio-medical as recurrent neural network, image convolutional neural network, long classification short term memory, and gated recurrent unit

Year Ensemble method

Area

Biomedical image classification

97.62% for brain image and 95.24% for chest X-ray

2022 Deep ensemble strategy

Three MRI and 98.51% in the one fMRI public binary case, and datasets 98.67% in the multiclass case

Alzheimer’s disease

2022 Multi-level context and uncertainty Medical image Breast cancer aware breast cancer histology image dataset

98.11%

2020 Snapshot boosting (LSTM, ResNet Natural IMDB dataset, 32, DenseNet 40) language CIFAR-10 processing and dataset computer vision tasks

91.52, 95.12, 94.31

2020 ResNet

Online and offline boosting

CIFAR-10 dataset

91.8

2020 Stacking

Image classification

CIFAR-10 dataset

94.1

2019 DEN P system

EZ-BM areas

OCT image

2019 ResNet110

Image classification

CIFAR-10 data set 93.99

93.99

improved the performance of several approaches ranging from traditional bagging and boosting to more recent innovative approaches like implicit or explicit ensembles and diverse ensembles and also observed how deep ensemble methods are used in different sectors. While deep ensemble methods have been used in many fields. There are still many unresolved concerns that can be resolved in the future. Big data is still a major challenge to solve, however one might investigate the advantages of deep ensemble methods for learning designs utilizing methods such as implicit deep ensembles.

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3. Ruder S (2017) An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 4. García-Pedrajas N, Hervás-Martínez C, Ortiz-Boyer D (2005) Cooperative coevolution of artificial neural network ensembles for pattern classification. IEEE Trans Evol Comput 9(3):271–302 5. Galar M et al (2011) A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(4):463–484 6. Rodriguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619–1630 7. Hassan AR, Haque MdA (2016) Computer-aided obstructive sleep apnea screening from single-lead electrocardiogram using statistical and spectral features and bootstrap aggregating. Biocybern Biomed Eng 36(1):256–266 8. Oza NC, Tumer K (2008) Classifier ensembles: select real-world applications. Inf Fusion 9(1):4–20 9. Kim H-C, Pang S, Je H-M, Kim D, Bang S-Y (2002) Support vector machine ensemble with bagging. In: International workshop on support vector machines. Springer, pp 397–408 10. Tao D, Tang X, Li X, Wu X (2006) Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans Pattern Anal Mach Intell 28(7):1088–1099 11. Zhou Z-H et al (2002) Lung cancer cell identification based on artificial neural network ensembles. Artif Intell Med 24(1):25–36 12. Ha K, Cho S, MacLachlan D (2005) Response models based on bagging neural networks. J Interact Mark 19(1):17–30 13. Tama BA, Lim S (2021) Ensemble learning for intrusion detection systems: a systematic mapping study and cross-benchmark evaluation. Comput Sci Rev 39:100357 14. Ganaie MA, Hu M (2021) Ensemble deep learning: a review. arXiv preprint arXiv:2104.02395 15. Cao C et al (2018) Deep learning and its applications in biomedicine. Genom Proteom Bioinform 16(1):17–32 16. Chen C, Xiong Z, Tian X, Zha Z-J, Wu F (2019) Real-world image denoising with deep boosting. IEEE Trans Pattern Anal Mach Intell 17. Yang B, Yan J, Lei Z, Li SZ (2015) Convolutional channel features. In: Proceedings of the IEEE international conference on computer vision, pp 82–90 18. Mosca A, Magoulas GD (2017) Deep incremental boosting. arXiv preprint arXiv:1708.03704 19. Zhang W, Jiang J, Shao Y, Cui B (2020) Snapshot boosting: a fast ensemble framework for deep neural networks. Sci China Inf Sci 63(1):112102 20. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778 21. Siu C (2019) Residual networks behave like boosting algorithms. In: 2019 IEEE international conference on data science and advanced analytics (DSAA). IEEE 22. Cortes C, Gonzalvo X, Kuznetsov V, Mohri M, Yang S (2017) AdaNet: adaptive structural learning of artificial neural networks. Technical report 23. Singh AP, Kumar S, Kumar A, Usama M (2022) Machine learning based intrusion detection system for minority attacks classification. In: 2022 international conference on computational intelligence and sustainable engineering solutions (CISES), pp 256–261. https://doi.org/10. 1109/CISES54857.2022.9844381 24. González S, García S, Del Ser J, Rokach L, Herrera F (2020) A practical tutorial on bagging and boosting based ensembles for machine learning: algorithms, software tools, performance study, practical perspectives and opportunities. Inf Fusion 64:205–237 25. Forootan MM et al (2022) Machine learning and deep learning in energy systems: a review. Sustainability 14(8):4832 26. Xia Y et al (2018) A novel heterogeneous ensemble credit scoring model based on bstacking approach. Expert Syst Appl 93:182–199

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Stress Detection Based on Multimodal Data in a Classroom Environment T. Swapna , A. Sharada , and M. Madhuri

Abstract Mental stress is a major problem these days, especially among teenagers. Mental health problems affect students’ health. They affect many areas of a student’s life, affecting the quality of life, academic performance, physical health and negatively affecting relationships with friends and family. These problems can affect future employment, income potential, overall health, and can also have long-term effects on the students. Early detection can help them before they go into depression and offer remedial measures that might help to relieve stress. Unfortunately, there is no real-time technique for automatic, continuous, consistent, and reliable stress detection at an early stage. We provide a new framework for instantaneous stress detection. This framework detects hu-man stress based on facial expressions, facial cues (stress score), and breathing patterns (respiration rate). Facial expressions of anger, disgust, fear, and sadness are all signs of stress. The distance between eyebrows and lip movement can also be used as stress indicators. Data on breathing patterns is critical for mental health analysis because it can detect early signs of stress and depression. Skeletal tracking with an Intel depth-sensing camera is used to collect this data. Finally, a machine learning model is built on the collected multimodal data to accurately predict stress levels in multiple subjects. Keywords Stress detection · Multimodal data · Respiratory rate · Facial cues · Stress Classification · Facial expressions

1 Introduction Recently, the word “stress” has been used frequently. The person’s reaction to the event determines the degree of stress. This response is triggered solely by biologically-produced hormones that sensitize the brain, tighten muscles, and increase heart rate. This quick response, commonly referred to as the “Fight-orFlight” mode, is necessary for survival and to deal with stressful situations. When T. Swapna (B) · A. Sharada · M. Madhuri G. Narayanamma Institute of Technology and Science, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_14

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people are stressed, they are not only affected psychologically, but also physically, emotionally, and behaviorally as well. Stress can be divided into two categories: good stress and bad stress and the four different levels of stress. Acute: This stress cannot be considered as dangerous because most of the stress is like “Fight-Flight” type. The most prevalent symptoms are past demands and stresses as well as anticipated demands and pressures soon. Episodic: If stress occurs very often then that type of stress is considered as Episodic. Symptoms are like disorderly, always in a rush, taking on too much, selfinflicted. Chronic: This stress is considered as dangerous as the person stress level reach too high which leads to suicide or harm to others or any danger can be taken. Examples are stress of poverty, dysfunctional families, unhappy relationships, no-way-out situation. Post Traumatic: This stress lacks the person’s confidence which is very dangerous to the human brain. Examples like sexual assault, warfare, any accidents, scared incidents, etc. The physiological stress response begins with the body’s response to the perception of the presence of the stressors, followed by specific sympathetic and hormonal responses that send signals to the brain to eliminate, reduce, or manage stress. Some of the most common symptoms are an increased heart rate, increased sugar and fat levels, and decreased bowel movements. Physical traits including altered facial expressions, increased breathing rate, increased blood flow to the forehead and the area sur- rounding the eyes, and electrical conductivity of the skin surface can be used to anticipate the presence of stress [1]. Physiological sensors play an important role in the detection of stress. Analyzing sensor data is a huge task for data analysts, but it provides an objective measure of physiological signals. Physiological cues have significant advantages compared to other resources such as emotional resources (facial expressions and verbal utterances). Many wired/wireless and contact/contactless sensors are currently available on the market. For example, in the health field, such as heart rate monitors, body temperature sensors, and fitness sensors, the requirements for sensors in health moni toring systems are portable, low power consumption, and user-friendly. Depth sensing technology is a new technology. In depth image each pixel represents the distance between the image plane and the corresponding object in the RGB image. There are numerous depth sensors available on the market, including Microsoft Kinect V1 & V2, Intel RealSense D415 & D435, Azure Kinect, Orbbec Astra (PRO), AsusXtion 2, PrimeSense Carmine 1.08 [2]. Traditional stress detection systems look for symptoms such as headaches, chest pain, abdominal pain, muscle pain, digestive problems, reproductive problems, changes in heart rate and blood pressure. In this way, stress symptoms can cause other illnesses and most symptoms can be faked/misunderstood. Wearable sensors are now being used to create smart watches that help detect stress based on heart rate. Still, this gives accurate values but must be worn every time to monitor the stress of the human which is uncomfortable sometimes. (see Fig. 1).

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Fig. 1 Physiological sensors

The proposed system uses non-wearable sensors to detect stress without disturbing people or misleading them with false symptoms. The system uses facial expressions, facial cues, and respiratory rate from non-wearable sensors such as webcams with depth sensing technology, Intel Real sense D435 cameras. Depth measurement technology provides 3-axis information: x-axis, y-axis, and z-axis (length between object and sensor).

2 Related Work The definition of stress is still being debated. Many researchers have been working to find human stress levels in different environments. Being stressed or not is so easy to recognize without considering many characteristics. Considering only emotional features that can produce manipulated results, the results are accurate when physiological features such as respiratory rate and facial problems are considered along with emotional features. Stress is hard to find with fewer features. Some researchers are using sensors such as contact/non-contact devices to help capture these physiological properties. A lot of research has been done in this area. Jing Zhang et al. (2022), suggested a real-time deep learning framework that fuses ECG, voice, and facial expressions for the purpose of detecting acute stress, the framework uses ResNet50 and I3D’s temporal attention module (TAM) to extract information about stress from the corresponding input. TAM can draw attention to the distinct temporal representation of stress-related facial expressions. The multimodality information concerning stress is then combined using the matrix eigenvector-based technique [3]. Shigang Li(2020) developed a framework for remotely detecting and classifying human stress by using a KINECT sensor that is portable and affordable enough for ordinary users in everyday life. Unlike most emotion recognition tasks in which

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respiratory signals (RSPS) are usually used only as an aiding analysis, the whole task proposed is based on RSPS [4]. Panagiotis Kostopoulos et al. (2016) developed the stress detection system Stay Active, which uses sleep patterns, physical activity, and social interaction to detect stress. A study applied to young adults and those working for a transport company in Geneva. The main motivation for this study is to work on how to calculate stress levels in the least invasive for users [5]. Gimpel et al. (2015) developed the application, my stress, as a stress detection system based on hardware and software sensor data collected using an android application. Researcher reads 36 hardware and software sensors to infer users perceived stress. By analyzing data from test users, at first sight into the feasibility of unobtrusive, continuous stress assessments considered exclusively from smartphone sensors [6]. Alireza Bolhariet al. (2012) studied workplace stress in IT professionals and compared stress levels and relationships between IT professionals. Research is conducted on 236 IT employees. The mean occupational stress levels of female and male professionals were 134 and 123, respectively. 91.5% of respondents did not participate in stress management courses. In this research, the researcher has found four hypotheses which show the level of stress the employees are affecting [7]. Using actual smartphone conversations, Hong Lu and colleagues (2012) created the stress sense app, a human voice-based stress detection system. Stress Sense is the way to detect the stress on person voice by using android mobiles and run in real-time. By using this researchers were able to achieve 81%–76% [8]. G. Giannakakis et al. (2017) developed a framework for the detection and analysis of stress/anxiety emotional states through video-recorded facial cues. Features under investigation included eye-related events, mouth activity, head motion parameters, and heart rate estimated through camera-based photo plethysmography. The results indicated that specific facial cues, derived from eye activity, mouth activity, head movements, and camera-based heart activity achieve good accuracy and are suitable as discriminative indicators of stress and anxiety [9]. In their study of college students’ social lives, Jong-Ho Kim et al. (1992) emphasized the benefits of physical activity in lowering stress levels. Interviewing nine university students for a qualitative study, the researcher employed 24 initial themes before narrowing them down to 8 main categories: self-efficacy, positive mood, mind and body, health behaviors, self-esteem, leisure, problem-focused coping, and positive expectation. Once more, these six key elements were distilled down and prioritized: pleasant mood, unification of the mind and body, increased self-esteem, leisure, problem-focused coping, and self-regulation of health behavior. This study seems important since it implies that frequent physical activity during free time can help people cope with problems effectively by making them feel pleased [10]. In previous work, stress was detected based on multimodal data. Sensors in different combinations were used and were able to achieve good to moderate accuracy. In this proposed system facial emotion, facial cues, and respiratory rate are used for the detection of stress and capable of attaining better accuracy.

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Fig. 2 Architecture of stress detection system

3 Methodology 3.1 Architecture These days’ stress has been a major problem for any age group of people. Irrespective of the age everyone is experiencing stress in a different way. This stress is leading to many health problems, especially people who are facing mental challenges. Thus, it’s critical to continuously assess and effectively manage stress. Hence a system is proposed to detect stress using facial expressions, facial cues, and their breathing patterns as indications to identify whether the person is in stress or not. The proposed solution has three main components. as shown in Fig. 2. The modules are Face Emotion Recognition, Facial cues (Stress Value), and Respiration Rate. Procedure of the system is as follows:

3.2 Module Description Classroom Environment Setup: Setting up the classroom environment in such a way that a depth camera should cover 5 students. Three students in a row and 2 students randomly between the three students’ row so that the student thorax joint

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Fig. 3 Classroom setup

should be visible to the camera (see Fig. 3). A web camera can be installed to capture facial expressions and cues. Face Emotions Module: By using this module, students’ seven emotions like anger, disgusted, sad, surprise, happy, neutral, and fearful will be recognized. collecting facial emotions is one of the attributes in the dataset. Facial emotions module involves the sub-modules like Face dataset, train dataset, face detection, and emotion recognition modules. We used deep face library for facial recognition and facial emotion recognition as it provided great accuracy. Deep Face is a facial recognition system developed by a Facebook research team. The model uses a nine-layer neural network with about 120 million link weights and was trained on four million photos shared by Facebook users. The Labeled Faces in the Wild (LFW) dataset shows that the Deep Face approach has an accuracy of 97.35% 0.25%, according to the Facebook Research team [11]. This model can even recognize multiple faces. Figure 4 shows the output of facial emotion module.

Fig. 4 Output of facial emotions module

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If a person’s facial expression is “scared” or “sad” or “angry” then we classify that person as Stressed otherwise we classify that person as Not Stressed. The facial expression is stored in CSV file after label encoding. Facial Cues Module: This module is to locate eyebrows, lips on the face and to find the distance between eyebrows and lip moment. This API detects stress using facial recognition employed by OpenCV, CNN, and Flask. The steps in calculating stress value from Facial Cues are: 1. Recognizing the face from the frame 2. Identifying Eyebrows and lips 3. Real-time stress calculation. For calculating stress value, we consider 8 real value parameters which belong to eyerows and lips [12]. 1. Eyebrow raise distance–The distance between the lower central tip of the eyebrow and the place where the upper and lower eyelids converge. 2. Upper eyelid to eyebrow distance–The separation between the surface of the upper eyelid and the eyebrow. The distance between the bottom central tips of each eyebrows is known as the inter-eyebrow distance. 3. Upper eyelid–The separation between the upper and lower eyelids. 4. Top lip thickness: A measurement of the top lip’s thickness. 5. Lower lip thickness–The measurement of the lower lip’s thickness. 6. Mouth width–The separation between the lip corner tips. 7. Mouth opening–The space between the upper and lower surfaces of the top lip. The seven binary parameters and the eight normalized real-valued parameters were fed into neural networks. The full dataset from 97 participants (467 photos) was split into three groups: 25 subjects (139 images) were used for training, 10 subjects (46 images) were used for preliminary testing, and 62 subjects (282 images) were used for the final analysis. We use normalized values of eyebrows and lips movement in calculation of stress value. A threshold value of 65 is chosen. if the stress value calculated by is more than 65, we label it as High Stress otherwise we label it as Low Stress for displaying. The stress value is taken into CSV file and Fig. 5 shows the output of Facial cues module. Respiration Rate Module: The third module, which is also an important aspect of this system is a respiration rate calculating module. When a person be- comes hyper during stressful situations, it causes abnormal breathing difficulties and can lead to dangerous diseases. This module makes use of Cubemos skeleton tracking module, which enables 2D/3D posture estimation on images and is particularly compatible with Intel’s RealSense D435 (See Fig. 6). By using D435 depth camera and cubemos software Skeleton data is extracted and thoracic joint values are stored in a csv file. By setting the camera to operate at a frame rate of 30fps, frame interval is calculated using: frame interval = 1/fps = 0.0333

(1)

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Fig. 5 Output of facial cues module

Fig. 6 Output of respiratory module

The difference between two successive breaths in the frame sequences is used to compute respiratory rate (N). Let Zi will be a vector with length n for a number of consecutive breaths Zi = [Zi(1), Zi(2), Zi(3).....Zi(N)]

(2)

For the purpose of comparing nearby elements of Zi, let X = diff([Zi[n],Zi[n + 1]) returns an N-1-dimensional vector. To find where Non-Zero values are located in X, apply Y = find(X) that produces an M-length vector with non-Zero values. The vector of the difference between the Y values, multiplied by the frame interval, can measure the vector of Respiratory cycles(Rc) over time(t). For the next N sequence, repeat the above steps to obtain other respiratory cycles and so on. Finally, respiratory rate(breaths/min) is calculated by using the following relations [13].

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RCcurrent = Rc2 − Rc1

(3)

Respiratoryrate = 60s/Rc

(4)

Infants typically breathe between 30 and 60 times per minute, children between 22 and 28, teenagers between 16 and 20, and adults between 14 and 18 times per minute. Data Pre-processing: In this module, the data collected from the above three modules is pre-processed and normalized using Label Encoding and Z Score Normalization. Combining all Three Features Data: This module combines the pre-processed outputs of the three modules and stores them in a csv file along with student Id or name (See Fig. 7). Student name is useful to track the stress levels over a period of time and every instance is labeled manually by following the condition, if any two modalities are predicted to be stressed then label is given as 1 otherwise 0. Dataset: Dataset is prepared with twenty subjects [students]. Dataset contains five features such as Person Name, RR, Stress Value (by Facial cues), Facial Expression

Fig. 7 Collected dataset

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and class label attribute. The entries in the dataset are manually labeled. Among Respiratory Rate, Stress value and Expression if any two are predicting stress then the output is labelled as Stressed (1), otherwise Not Stressed (0). Classification & Training: Naïve Bayes classifier is used for training the model on the collected and manually labeled data. This classifier is a basic one and also very effective one. The attributes RR, Stress value and Expression act as conditional probability and output is taken as class probability. Prediction: After training the model this module takes the input from web Camera and intel RealSense D435 and predicts the output. The output of the predict module is classified as “Stressed” or “Not Stressed.” If a person is detected as stressed then output result is shown on screen as stressed, otherwise shows Not Stressed.

4 Results The user interface gives us the results for facial recognition, facial expression, and facial cues. The Face recognition and face expression outputs are stored in a csv formatted file. The output of the facial cues is shown on the GUI screen itself (See Fig. 8). Accuracy and predicted values are shown in Fig. 8.

5 Conclusions and Future Work Stress is identified using physiological characteristics such as breathing rate, facial cues, and facial expressions. In conventional methods, the person was defined as stressed or normal, which is a biased assessment, based on information gathered from perceptive individuals like themselves, physiological experts, and outside observers. For all aspects including face emotions, respiration rate, and facial cues, data is collected via Intel Real sense D435 and web camera. The Nave Bayes method is used to determine whether the person is stressed or normal. Respiration rate, stress score, and facial expressions are taken into account in the dataset, with stress serving as the target characteristic.

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Fig. 8 Results of classification report and predictions

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References 1. Panure T, Sonawani S (2019) stress detection using smartphone and wearable devices: a review. Asian J Converg Technol. https://doi.org/10.33130/AJCT.2019V05I01.007 2. Intel Depth Sense Technology, https://www.intelrealsense.com/depth-camera-d435/ 3. Zhang J, Yin H, Zhang J, Yang G, Qin J, He L (2022) Real-time mental stress detection using multimodality expressions with a deep learning framework. Front Neurosci 16:947168. https:// doi.org/10.3389/fnins.2022.947168 4. Shan Y, Li S, Chen T (2020) Respiratory signal and human stress: non-contact detection of stress with a low-cost depth sensing camera., Int J Mach Learn Cybemetics 11(11), 1825–1837. https://doi.org/10.1007/s13042-020-01074-x 5. Kostopoulos, Panagiotis, Kyritsis, Athanasios, Deriaz, Michel, Konstantas Dimitri (2016) Stress detection using smart phone data, lecture notes of the institute for computer sciences, social informatics and telecommunications engineering book series, pp 340–351, https://doi. org/10.1007/978-3-319- 49655–9_41 6. Gimpel et al. (2015) My Stress: Unobtrusive smartphone—based Stress detection, In: Proc. the 23th European Conference on Information Systems, Münster, Germany 7. Bolhari A Rezaeian, Bolhari J, Bairamzadeh S (2012) Occupational stress level among information technology professionals in iran, international journal of information and electronics engineering, 2(5),2010-3719 8. H. Lu et al. (2012) Stress Sense: Detecting stress in unconstrained acoustic environments using smartphones, In: Proc. ACM Conference on Ubiquitous Computing, pp. 351–360, https://doi. org/10.1145/2370216.2370270 9. Giannakakis G, Pediaditis M, Manousos D, Kazantzaki E, Chiarugi F, Simos PG, Marias K, Tsiknakis M (2017) Stress and anxiety detection using facial cues from videos, Biomedical Signal Processing and Control, Volume 31, Pages 89–101, 1746–8094, https://doi.org/10.1016/ j.bspc.2016.06.020 10. Kim J-H et al. (2014) The Impacts of physical exercise on stress coping and well-being in university students in the context of leisure, Journal on Heal, 6(19), https://doi.org/10.4236/ health.2014.619296 11. Deep face interface:https://viso.ai/computer-vision/deepface/ 12. Ying-Li Tian, Takeo Kanade, Jeffrey F Cohn, Facial Expression Analysis, Chapter-11 13. Shan Yuhao, Li Shigang, Chen T (2020) Respiratory signal and human stress: non-contact detection of stress with a low-cost depth sensing camera, Int J Mach Learn Cybern, 11. https:// doi.org/10.1007/s13042-020-01074-x

Comparative Study of Different Generations of Mobile Network Pooja Rani

Abstract Due to the development of latest techniques, wireless technology was invented. Basically, it is used in internet access, video conference, and entertainment of mobile technology. People can use the above techniques at any place and any time by mobile communication. Wireless communication means without wires we can transmit data over a long or short distance. In this paper, we will discuss about 1G, 2G, 3G,4G, and 5G technology advantages, disadvantages, and applications discussed. “G” stands for generation of networks. Recently, we have launched 5G technology in India and working on 6G for future aspects. Keywords 1G · 2G · 3G · 4G · 5G · 6G

1 Introduction The terms “mobile” and “wireless” are two very different concepts applied to new technology. Mobile word used for portable devices. Old computers or other nonmobile devices may be access wireless networks. It is made to be taken any place and at any time. For power, internal battery may be required and connected to mobile network without the use of hardware components for sending and receiving data [1]. First Generation, Second Generation, Third Generation, Fourth Generation and Fifth Generation are part of wireless mobile network shown in Fig. 1. The firstgeneration (1G) handled without wire network uses only phone calls [2]. In the second generation (2G) used only digital and text messages. Expansion of digital data, high performance, and increment of data processing are used by Third-generation technology (3G). The fourth-generation (4G) used less cost with more bandwidth [3]. The fifth generation (5G) Worked on advanced and best technology for high bandwidth and good networks. Wireless local area networks, wireless metropolitan area networks, wireless personal area networks, and wireless wide area networks are different types of wireless network [4]. The standard of the 5G communications is P. Rani (B) G L Bajaj Institute of Technology and Management, Greater Noida, UP, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_15

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Fig. 1 Different mobile generations

1G 2G

6G

5G

3G 4G

almost over and deployment has commenced globally. A new standard of the sixthgeneration (6G) wireless network will have the full provision of AI and is projected to be deployed between 2027 and 2030 [8]. There are many advantages of wireless networks are cheaper for installation and maintenance, high speed of data transfer, and used at any time and place. There are many disadvantages like communication by open space, less security, unreliable and less speed of transformation. Examples of Wireless Devices are TV remote, radio, phone, tablets, wireless routers that don’t use wires for transmission of information.

1.1 First Generation (1G) In 1979, Nippon Telegraph and Telephone launched First Generation (1G) in Japan, it became the first nationwide 1G network within five days. It used analog signals, first-time voice calling in mobiles with bandwidth of 25 MHz, no roaming, small coverage area, quality of sound was low, and with speed 24Kbps. Advanced Mobile Phone System (AMPS) was the first public cellular telephone system [6]. Introduced in 1979 in the United States. Other cellular systems were TACS, NMT, C450, etc., introduced in 1979. The advantages of 1G network were sound clarity, use of analog signal, reduction of noise, secure and safe information and voice calls, and low consumption of battery power. The disadvantages of a 1G network are the bad quality of sound, bigger size phone, poor battery life, interference of signals, poor reliability, less speed, and limited capacity [5].

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1.2 Second Generation(2G) In 1991, The second generation of mobile networks, or 2G, was launched under the GSM standard. When 1G upgraded to 2G then cell phones upgraded by digital signals. The main function of 2G Generation was to provide SMS and MMS (Multimedia Message), better voice quality, increase data speed up to 64 kbps, and bandwidth of 30 to 200 kHz. In 1991, Finland by Radioing launched Second generation 2G cellular. In this generation, still we use basic services like SMS, internal roaming, conference calls, call hold. Call charges effected by distance and call time. The main drawback of Second Generation(2G) was too unable to video call and required strong digital signal [6].

1.3 Third Generation (3G) On 1 October 2001, Third Generation (3G) was launched by NTT DoCoMo in Japan based on W-CDMA technology. 3G technologies provide more capable and flexible as compared to 2G. It includes two-way communications with high bandwidth and more stable. It includes code division multiple access (CDMA), time division multiple access (TDMA), and Global System Mobile (GSM, a variation of TDMA). The main function was to provide more bandwidth, security, and reliability as compared to second generation. It provides new radio signal to reduce congestion in the present system. It gives fixed and variable data rates. It provides faster transfer of data and less cost as compared to 2G. 3G finds application in wireless voice call, video call, Internet access of mobile, fixed wireless Internet access, and mobile TV. The main drawback was to required different handsets, high power consumption, and roaming and data/voice work together has not yet been implemented [5].

1.4 Fourth Generation (4G) 4G launched by TeliaSonera, in the capitals of Sweden and Norway–Stockholm and Oslo on December 14, 2009. 4G provides 10 times faster speed as compared to 3G. It provides smartphone as compared to PC. It gives multimedia and gaming capabilities. It uses OFDMA instead of CDMA. In this, digital signal is divided into different contracted frequencies, modulated by data. It supports voice, video, wireless internet, and multimedia. Its speed and capacity were high as compared to 3G. It provides higher speed of data transfer, faster response time and increase overall network capacity. With 4G download speeds, wireless users got high-definition video and audio. It includes high-definition TV, gaming channels, video conference call. More consumption of battery as compared to 3G. Limited 4G network towers, high data consumption, and high cost were the limitations [6].

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1.5 Fifth Generation (5G) 5G launched in Chicago and Minneapolis on April 3, 2019. Qualcomm made 5G technology. Reliance Jio Chairman Mukesh Ambani launched 5 g first in India. 5G is still in the testing phase for 5G Network in India. 5G main features are high capacity and more bandwidth as compared to 4G. 5G is a faster network as compared to other networks. This network makes businesses more efficient. The limitation is radio frequency becomes a problem and increased bandwidth will mean less coverage. It provides high download speed, work on smart city, traffic management, and autonomous vehicles [7].

1.6 Sixth Generation (6G) The contribution of 6G communication will continue in the developments of the preceding generations and comprises innovative services along with emerging technologies. These innovative new services embrace AI, smart wearables, self-driving vehicles, computing reality devices, sensor devices, and 3D mapping (Saad et al. 2019) [9]. 6G wireless networks play a vital role due to their high efficiency in managing a huge amount of data and transmitting the data at a high rate (Mumtaz et al. 2017) [10]. The articles listed in Table 2 elaborate on the role of AI/ML/DL in 6G wireless technologies.

2 Different Mobile Generation Different generations of advantages, disadvantages, and applications are discussed in Table 1 and different parameters of different generations are discussed in Table 2.

3 Discussion and Future Scope We discussed about different generations of communication in this paper. 5G is now (not widely) available in India but widely used worldwide now. It is expected that in the next 5 to 8 years, 6G wireless technique will be available. Beyond 5G, the major aspects to be considered are increased system capacity, more throughput, lower latency, enhanced security, and improved quality of service (QoS) over present 5G technologies. In the future, the scope in 6G is there with fully assistance of the Artificial Intelligence technique. Although, there are lots of challenges, but work is going on in this area and this is a prominent research area.

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Table 1 Advantages, disadvantages, and applications of different generations Generations

Advantages

Disadvantages

Applications

1G [5, 6]

Voice calling, no roaming, low consumption of battery

bad quality of sound, bigger size phone, poor battery life, interference of signals, poor reliability, less speed and limited capacity

Voice calling in mobiles

2G [6]

better voice Unable to video call quality, increased data speed

3G [5]

more bandwidth, security, and reliability

required different handsets, high in wireless voice call, video power consumption, and roaming call, Internet access of mobile, fixed wireless Internet access, and mobile TV

4G [6]

faster speed and high capacity

4G network towers, high data consumption, and high cost

Smartphone, multimedia, and gaming ability

5G [7, 12]

More bandwidth, security, and reliability

Less coverage and radio frequency problem

work on smart city, traffic management, and autonomous vehicles

6G [9, 10]

Provide a Difficult to use 1000 times faster data rate than 5G

SMS and MMS facility

Help in real time applications like human surgery through remote areas

4 Conclusion Now a day mobile phone is the body part of the human body. Without mobile phone, person feels helpless. He is totally dependent on smartphone due to the various applications listed above in Table 1. l We know that features of mobile phone vary from generation to generation, advantages, and disadvantages. In this paper we conclude that how generations vary with so many advantages, limitations, and applications. Now we see that due to coronavirus, every child uses the internet to attend online classes. But the use of 5G effect the radio frequencies.

800–900 MHz

Frequency

FDMA

Multiple access

1970–84

Analog voice

Service

Circuit

1.9kbps

Data Bandwidth

Starts from

Analog technology

Requirements

Switching

1G

Technology

850- 1900 MHz

Circuit Packet

1990

TDMA CDMA

Digital voice

14.4 to 384kbps

Digital technology

2G

Table 2 Different parameters of different generation [11]

1.6–2.5GHZ

Circuit Packet

2001

CDMA

Integrated high quality audio, video, and data

2Mbps

ITU’S IMT

3G

2-8GHZ

Packet

2010

CDMA

HD Streaming and Roaming

2Mbps to 1Gbps

ITU’S IMT advanced

4G

600–900 MHz

All packet

2015

CDMA & BDMA

HD streaming, upcoming all technologies, and global roaming smoothly

1Gbps and high

Mobile cloud service

5G

up to 7.125 GHz

All Packet

2015 onwards

Terahertz or submillimeter

Provide microsecond latency communications

100 Gbps to 1 Tbps

(uHSLLC), (uHDD)

6G

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References 1. Henry H Wilmer, Lauren E Sherman, Jason M Chein (2017) Smartphones and cognition: a review of research exploring the links between mobile technology habits and cognitive functioning, 8 2. Pankaj Sharma (2013) IJCSMC. Evolution of Mobile Wireless Communication Networks-1G to 5G as well as Future Prospective of Next Generation Communication Network 2(8):47–53 3. Robin Chataut, Robert Akl Sensors (2020) (Basel) Massive MIMO Systems for 5G and beyond Networks—Overview, Recent Trends, Challenges, and Future Research Direction 20(10): 2753 4. Asvin Gohil, Hardik Modi Shobhit K Patel (2013) International Conference on Intelligent Systems and Signal Processing (ISSP) , 5G technology of mobile communication: A survey 5. Bakare BI, Bassey EE (2021) A comparative study of the evolution of wireless communication technologies from the first generation (1G) to the fourth generation (4G). Int J Elect Commun Comp Eng 12(3):2249–071X , ISSN (Online) 6. Khalil Ullah, Muhammad Riaz, Abdul Qadir Khan, Anas Bilal (2016) Comparative analysis of mobile generations regarding technical aspects. Int J Multid Sci Eng 7(4) 7. Mr Vinayak Pujari, Dr Rajendra Patil Mr Kajima Tambe (2021) Research paper on future of 5G wireless system, contemporary research in India (ISSN 2231–2137): Special Issue 8. Singh P, Agrawal R, Singh KK (2021) Machine learning based user retention and channel allocation: 6G Aspect 9. Saad W, Bennis M, Chen M (2019) A vision of 6G wireless systems: applications, trends, technologies, and open research problems. IEEE Network 34(3):134–142 10. Mumtaz S, Jornet JM, Aulin J, Gerstacker WH, Dong X, Ai B (2017) Terahertz communication for vehicular networks. IEEE Trans Veh Technol 66(7):5617–5625 11. Vaigandla KK, Bolla S, Karne R (2021) A survey on future generation wireless communications-6G: requirements, technologies, challenges and applications. Int J Adv Trends Comp Sci Eng 12. Datta P, Rohilla R (2019) Optimization and performance matrices in 5G networks. 2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC), pp 312–317. https://doi.org/10.1109/PEEIC47157.2019.8976723

A Novel Framework for VM Selection and Placement in Cloud Environment Krishan Tuli and Manisha Malhotra

Abstract In spite of various research that has been conducted in the past but there are some challenges that are still into existence related to balancing of workload in cloud applications. There has been a great need for efficient allocation of resources that is handling all the data center, servers & various virtual machines connected with cloud applications. It is the responsibility of cloud facility providers to confirm high facility delivery in an unavoidable situation. All such type of hosts is overloaded or underloaded based on their execution time and throughput. Task scheduling helps in balancing the load of resources and on the other hand task scheduling adheres to the requirement of service level agreement. SLA parameters such as deadlines are concentrated on the Load Balancing algorithm. This paper proposes algorithm which optimizes cloud resources and improves the balancing of load based on migration, SLA and energy efficiency. Proposed load-balancing algorithm discourses all states and focuses on existing research gaps by focusing on the literature gaps. Task scheduling is mainly concentrating on balancing the load and task scheduling mainly adheres to SLA. SLA is one of the documents offered by the service provider to the user. There are various parameters of load balancing such as deadlines which are discussed in the load balancing algorithm. The key focus of proposed process identifies optimize method of resources and improved Load Balancing based on QoS, priority migration of VMs and resource allocations. This proposed algorithm addressed these issues based on the literature review findings. Keywords Cloud computing · Data center · Load balancing · QoS · Service level agreement (SLA) · Virtual machine (VM)

K. Tuli (B) · M. Malhotra University Institute of Computing, Chandigarh University, Mohali, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_16

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1 Introduction Due to more inclination towards the storage on online application and different services, cloud computing has become the backbone for every business nowadays. Cloud computing provides many services such as software on web browsers as a platform for designing and developing cloud-based applications [5]. It also provides infrastructure and backend resources are handled by the service providers like data centers. There exist many different service models which delivers resources [7]. Load balancing schemes are used to distribute the load around the servers and data centers. These schemes and approaches help the resources to manage Physical and Virtual Machines on cloud. An efficient load-balancing approach ensures that each resource can be utilized equitably which will help in multiple way. Basically, load balancing can be completed in 2 manners. Firstly, “Statistics Centric Load Balancing” and secondly, “Domain Name Centric Load Balancing” [6, 7]. The “domain Load balancers” are used to arrange the load to synchronization but on the other hand, static load balancers are used to deploy protocols or applications on the various application facilities. Load balancers help users to exploit and explore more products [9]. Following are the benefits of Load Balancers specified for Data Centres: (a) (b) (c) (d)

Scalability Cyber Spikes High functionality Low-cost hardware

Virtualization is the very essential feature of any cloud-based application. This technique helps in the performance and on-demand service provided to clients for smooth functioning of the application. It also helps in handling the migration and allocation process of VM resources efficiently [1, 4]. There are mainly three main challenges in cloud computing. This research paper will focus on the enhancement of resources in infrastructure service. This is a pivotal concept in load balancing. Users will access the cloud services by sending a request for a particular resource. The main aim of the cloud service provider is to provide the services and enhance user fulfillment [2, 3]. Thus, the proposed system focuses on the infrastructure model and hence it is dealing with the workload of the server. Basically, two parameters are there in cloud environment. User side is called frontend and backend handles service models. Cloud service models are used to service the users request and various data centers where multiple physical machines are stored. It is also known as servers [9, 10]. Users sending the requests from the applications are scheduled dynamically. Virtualization also aids in load balancing and efficiently allocating resources. Effective scheduling helps to reduce the execution time of the processes and increase resource utilization.

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Task scheduling is related to workload balancing as shown in Fig. 1. User sends the request and it is submitted through cloud broker [11]. The proposed algorithm must have efficient job allocations to appropriate virtual machine considering essential parameters like maintaining high quality service with the execution of requests sent. Another constraint is to complete the task within its specific requirement that will be provided in Service Level Agreement [13]. This task is highly dependent on the scheduling policy’s efficiency which is instructed to give high-efficiency results for load balancing among various hosts and servers. Efficient utilization of resources can be achieved using dynamic task scheduling in cloud environment. Hypervisor or Virtual Machine Monitor (VMM) is the backbone for any cloud environment as shown in the figure above. It acts as a middleware between the user and hosts [12, 14]. Basically, hypervisor helps in executing several virtual machines on a specific layer. Cloud virtualization is very important for cloud environment and efficient mapping of tasks. It quickly corrects the performance of all degraded cloud applications. Furthermore, this will result in

Fig. 1 Task scheduling in cloud environment

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Fig. 2 Virtualization in cloud computing

an unbalanced burden across all servers. Important characteristics such as QoS must be taken into account in order to achieve optimal resource use without compromising SLAs [15, 16]. Resource allocation is a challenging task in cloud computing and it leads to load balancing (Fig. 2).

2 Related Work The following section focuses on the related work of other authors. Author discusses load balancing perception and highlights its different models and metrics along with existing standard algorithm. Further, it leads to related work based on load distribution & proposes various algorithms. Load balancing is related to the optimization of resources. It is a unique technique for the similar distribution of work along with better employment of resources in an efficient manner. This is one of the crucial aspects of dynamic distribution of workload among various nodes connected to the network. Higher satisfaction and better resource allocations result from effective task balance. Sending and receiving data will be faster in a balanced cloud environment [17, 18]. It critically addresses load-balancing concepts & improves application performance. As the number of cloud users grows, there is a need to improve the work scheduling system, and Task Scheduling is the major purpose of load balancing [19, 20]. Thus, issues in task scheduling must be resolved by the various utilization algorithms and the same is discussed in the later section. It is a methodology to execute task efficiently so that

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all the resources will be utilized fully. Basically, in cloud environment, users utilize huge number of resources and manually it is not feasible to allocate each task [21]. Now-a-days, Cloud services becomes a pivotal part of every organization. Such corporations provide streamless data continuously. Though, the existing algorithms are working slower to respond and face some of the challenges as the number of hosts are increasing. It is a main feature of cloud technology and it will increase the delays and response requests. Such problems could jeopardize Service Level Agreements (SLAs) [23]. SLA is a legal agreement between the user and service provider that violates, if the service provider fails to provide stipulated service or service degrades [22]. Such violates of SLA will lead to starvation. Further, it is not able to process the incoming tasks properly. Hence it rejects all the incoming processes. Thus, such issues reduce SLA-V by vendors. As per [25], following are the factors due to which unbalanced issues are related: a. b. c. d.

Inefficient scheduling of tasks to resource. Inefficient scheduling process. Different requirements for different user tasks. Inappropriate distribution of tasks of all virtual machines.

Author [14] has proposed an algorithm based on load balancing to reduce makespan and appropriate usage of resources. It sorts all the tasks using the processing speed and length of the process with the help of the bubble sort algorithm. Further, the tasks are allocated to VMs using FCFS basis. After the allocation, load balancing is done considering the load of VMs. This process helps in easy optimization of resources. Author [15] proposes a load-balanced algorithm, i.e., ELBMM for utilization of resource. In this, least execution time is considered for VM allocation in least completion time. Proposed algorithm shows great results in least cost utilization. Author [18] proposes algorithm which is based on concept of load balancing placed on various layers of cloud model. In this, a hybrid concept has been inculcated by merging Load Balance Min-Min and Opportunistic Load Balancing. For this, ZEUS framework has been used for improving task scheduling. Tasks are assigned to any vendor. This approach is very beneficial to keep the nodes busy and to serve user requests efficiently. Author [21] has enhanced the Quality of Service (QoS) based on an algorithm allocates the cloudlets with an improved balancing technique so that completion time of cloudlets can be decreased. This algorithm insists in keeping active model of systems and balancing of workloads.

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In [27, 28], author has proposed an efficient scheduling algorithm which is very helpful in minimizing execution time. Minimizing the execution time of the process is very beneficial for users and vendors. Proposed algorithm helps in selecting an appropriate VM from various data centers. This procedure is named as “State-Based Load Balancing (SBLB)” that assigns the responsibilities among various hosts. This procedure produces higher execution time.

3 Proposed Work The proposed work algorithm is divided into two segments. The first segment is responsible for the placement of the VM into the respective clusters where a novel fitness function is derived utilizing the attribute architecture of Swarm Intelligence (SI). Usage of SI has been observed in a lot of recent articles [11–14]. The proposed algorithm identified that among most of the research cited articles, colony algorithms have made a significant impact over the selection and migration policy. The selection procedure gets incorporated at the IaaS layer when a user submits the request to the server. The server handler passes the requests to the scheduler and in order to support the elasticity, the VMs are migrated from one PM to another PM as shown in the overall system model Fig. 3.

Fig. 3 Overall system architecture

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The algorithm is executed in following steps. Step 1. Initialize allocation table to empty. Step 2. Divide data into k number of clusters utilizing Nashaat et al. Step 3. Use flower pollination for the up gradation of hosts containing the VMs. Step 4. Choose flower index. Step 5. Migrate VM to flower index. Step 6. Validate VM. Step 7. Store information to repository. Step 8. Use information to reduce further computation complexity.

4 Conclusion The author closes the research by presenting the results of the literature review, as well as a proposed load-balancing method and the placement of virtual machines into their corresponding clusters. Task scheduling adds to load balancing in cloud computing, as we saw in the literature review. Further, there is a need to balance the load by placement of VM into clusters for proper balancing of load. For better improvement in Load Balancing is a term used to describe the process of balancing Task scheduling ensures that cloud resources are used efficiently. The main goal of this work was to design a load-balancing system that was both efficient and effective, as well as the best feasible VM migration and placement mechanism. Literature survey concluded that it is important to reduce the makespan and further provide an efficient resource utilization. Functioning of proposed algorithm works very well in dynamic environment. This algorithm is developed in such a way that it can manage big demands. The suggested technique additionally handles VM SLA violations by efficiently allocating resources to jobs. Author will optimize cloud resources and improve the performance of cloud-based apps in the future. This algorithm will be checking the total number of migration and violation for better performance.

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Table 1 Comparative analysis of existing work based on nature inspired optimization algorithms Paper

Optimization algorithm

Objective

Optimized resources

Mi et al. (2010)

GA

It helps in improving CPU utilization and reduce number of PMs

RAM and CPU

Xu et al. [18]

GA

It avoids hotspots and reduces power consumption as well as wastage of resources

Memory and CPU

Wang et al. [13]

GA

Author minimizes CPU cycles, memory, communication traffic and bandwidth and maximizes resource utilization

Sofia et al. [22]

GA

Minimizing makespan & energy

CPU

Yousefipour et al. (2018)

GA

Author helps in tumbling active PMs & energy consumption

RAM and CPU

[27]

PSO

Author has enhanced the CPU, memory and resource utilization and bandwidth minimizes power consumption & migration time

Dashti and Rahmani [28]

PSO

It minimizes energy consumption and number of migrations

CPU and RAM

Gao et al. [13]

ACO

Improving overall resource utilization

CPU, Memory, Network I/O

Ferdaus et al. [31]

ACO

Improving energy efficiency, minimum resource wastage

CPU, Network and Storage

Wen et al. [32]

ACO

Minimizing SLA Violations

CPU, memory, and disk

Li et al. [35]

ACO

Author helps in optimizing number of migrations, SLA-V

Memory and CPU

Jiang et al. [37]

ABC

Author has enhanced the CPU, memory, and resource utilization and bandwidth minimizes power consumption & migration time

Li et al. [35]

ABC

Author has enhanced the CPU and memory resource utilization and minimizes migration time (continued)

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Table 1 (continued) Paper

Optimization algorithm

Objective

Optimized resources

Kansal et al. [39]

FA

Author reduces the cost of time and communication

RAM, CPU and bandwidth

Perumal et al. [40]

FA

Author reduces energy CPU and bandwidth consumption and cost of communication

Yavari et al. [1]

FA

Author helps in optimizing number of migrations, SLA-V and energy consumption

CPU and bandwidth

Cho et al. [14]

ACO, PSO

Minimizing number of migrations

CPU

References 1. Yavari M, Rahbar AG, Fathi MH (2019) Temperature and energy-aware consolidation algorithms in cloud computing. J Cloud Comput 8(1):1–16 2. Zhang P, Zhou M, Wang X (2020) An intelligent optimization method for optimal virtual machine allocation in cloud data centers. IEEE Trans Autom Sci Eng 17(4):1725–1735 3. Beloglazov A, Buyya R (2010) Energy efficient resource management in virtualized cloud data centers. In 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (pp. 826–831). IEEE 4. Le Sueur E, Heiser G (2010) Dynamic voltage and frequency scaling: the laws of diminishing returns. In Proceedings of the 2010 international conference on Power aware computing and systems (pp. 1–8) 5. Arroba P, Moya JM, Ayala JL, Buyya R (2017) Dynamic voltage and frequency scaling-aware dynamic consolidation of virtual machines for energy efficient cloud data centers. Concur Comput Pract Exp 29(10):e4067 6. Masdari M, Khezri H (2020) Efficient VM migrations using forecasting techniques in cloud computing: a comprehensive review. Cluster Computing, pp 1–30 7. Zhang F, Liu G, Fu X, Yahyapour R (2018) A survey on virtual machine migration: challenges, techniques, and open issues. IEEE Commun Surv Tutorials 20(2):1206–1243 8. Nashaat H, Ashry N, Rizk R (2019) Smart elastic scheduling algorithm for virtual machine migration in cloud computing. J Supercomput 75(7):3842–3865 9. Zhang J, Huang H, Wang X (2016) Resource provision algorithms in cloud computing: a survey. J Netw Comput Appl 64:23–42 10. Ferreto TC, Netto MA, Calheiros RN, De Rose CA (2011) Server consolidation with migration control for virtualized data centers. Futur Gener Comput Syst 27(8):1027–1034 11. Beloglazov A, Buyya R (2012) Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans Parallel Distrib Syst 24(7):1366–1379 12. Li X, Qian Z, Lu S, Wu J (2013) Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center. Math Comput Model 58(5–6):1222–1235 13. Song W, Xiao Z, Chen Q, Luo H (2013) Adaptive resource provisioning for the cloud using online bin packing. IEEE Trans Comput 63(11):2647–2660

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14. Hwang I, Pedram M (2013) Hierarchical virtual machine consolidation in a cloud computing system. In 2013 IEEE Sixth International Conference on Cloud Computing (pp. 196–203). IEEE 15. Zhang J, He Z, Huang H, Wang X, Gu C, Zhang L (2014) SLA aware cost efficient virtual machines placement in cloud computing. In 2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC) (pp. 1–8). IEEE 16. Shi L, Butler B, Botvich D, Jennings B (2013) Provisioning of requests for virtual machine sets with placement constraints in IaaS clouds. In 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013) (pp. 499–505). IEEE 17. Anshika Negi, Mayank Singh, Sanjeev Kumar (2015) Article: an efficient security farmework design for cloud computing using artificial neural networks. Int J Comp Appl 129(4):17–21. Published by Foundation of Computer Science (FCS), NY, USA 18. Xu J, Fortes JA (2010) Multi-objective virtual machine placement in virtualized data center environments. In 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing (pp. 179–188). IEEE 19. Wang S, Gu H, Wu G (2013) A new approach to multi-objective virtual machine placement in virtualized data center. In 2013 IEEE Eighth International Conference on Networking, Architecture and Storage (pp. 331–335). IEEE 20. Kumar S, Karnani G, Gaur MS, Mishra A (2021) Cloud security using hybrid cryptography algorithms. 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), pp. 599–604. https://doi.org/10.1109/ICIEM51511.2021.9445377 21. Liu C, Shen C, Li S, Wang S (2014) A new evolutionary multi-objective algorithm to virtual machine placement in virtualized data center. In 2014 IEEE 5th international conference on software engineering and service science (pp. 272–275). IEEE 22. Sofia AS, GaneshKumar P (2018) Multi-objective task scheduling to minimize energy consumption and makespan of cloud computing using NSGA-II. J Netw Syst Manage 26(2):463–485 23. Riahi M, Krichen S (2018) A multi-objective decision support framework for virtual machine placement in cloud data centers: a real case study. J Supercomput 74(7):2984–3015 24. Yousefipour A, Rahmani AM, Jahanshahi M (2018) Energy and cost-aware virtual machine consolidation in cloud computing. Software: Pract Exp 48(10):1758–1774 25. Guo L, He Z, Zhao S, Zhang N, Wang J, Jiang C (2012) Multi-objective optimization for data placement strategy in cloud computing. In International Conference on Information Computing and Applications (pp. 119–126). Springer, Berlin, Heidelberg 26. Xu B, Peng Z, Xiao F, Gates AM, Yu JP (2015) Dynamic deployment of virtual machines in cloud computing using multi-objective optimization. Soft Comput 19(8):2265–2273 27. Wang S, Zhou A, Hsu CH, Xiao X, Yang F (2015) Provision of data-intensive services through energy-and QoS-aware virtual machine placement in national cloud data centers. IEEE Trans Emerg Top Comput 4(2):290–300 28. Dashti SE, Rahmani AM (2016) Dynamic VMs placement for energy efficiency by PSO in cloud computing. J Exp Theor Artif Intell 28(1–2):97–112 29. Li H, Zhu G, Cui C, Tang H, Dou Y, He C (2016) Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing. Computing 98(3):303–317 30. Gao Y, Guan H, Qi Z, Hou Y, Liu L (2013) A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J Comput Syst Sci 79(8):1230–1242 31. Ferdaus MH, Murshed M, Calheiros RN, Buyya R (2014) Virtual machine consolidation in cloud data centers using ACO metaheuristic. In European conference on parallel processing (pp. 306–317). Springer, Cham 32. Wen WT, Wang CD, Wu DS, Xie YY (2015) An ACO-based scheduling strategy on load balancing in cloud computing environment. In 2015 Ninth International Conference on Frontier of Computer Science and Technology (pp. 364–369). IEEE 33. Tan M, Chi C, Zhang J, Zhao S, Li G, Lü S (2017) An energy-aware virtual machine placement algorithm in cloud data center. In Proceedings of the 2nd International Conference on Intelligent Information Processing (pp. 1–9)

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34. Malekloo MH, Kara N, El Barachi M (2018) An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments. Sustain Comput Inf Syst 17:9–24 35. Li Z, Yan C, Yu L, Yu X (2018) Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method. Futur Gener Comput Syst 80:139–156 36. Liu F, Ma Z, Wang B, Lin W (2019) A virtual machine consolidation algorithm based on ant colony system and extreme learning machine for cloud data center. IEEE Access 8:53–67 37. Jiang J, Feng Y, Zhao J, Li K (2017) DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model. Futur Gener Comput Syst 74:132–141 38. Li XK, Gu CH, Yang ZP, Chang YH (2015) Virtual machine placement strategy based on discrete firefly algorithm in cloud environments. In 2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) (pp. 61–66). IEEE 39. Kansal NJ, Chana I (2016) Energy-aware virtual machine migration for cloud computing-a firefly optimization approach. J Grid Comp 14(2):327–345 40. Perumal B, Murugaiyan A (2016) A firefly colony and its fuzzy approach for server consolidation and virtual machine placement in cloud datacenters. Adv Fuzzy Syst 41. Cho KM, Tsai PW, Tsai CW, Yang CS (2015) A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing. Neural Comput Appl 26(6):1297–1309

Quad Clustering Analysis and Energy Efficiency Evaluation in Wireless Sensor Networks Bhawnesh Kumar, Sanjiv Kumar, Harendra Singh Negi, and Ashwani Kumar

Abstract In wireless sensor network (WSN), energy usage of each node is a main concern to enhance the network performance. A sensor node in wireless sensor networks (WSN) may directly or indirectly communicate to the base station (BS) through single or multi-hop. In that case, the whole network can elect the cluster head to collect the sense data from the remaining nodes of a network. Aggregation is performed at the end of the head node and combined data send to BS. The whole network can also be divided into tiny sensor networks which groups are known as clusters. When clusters are formed then also apply the cluster head (CH) selection mechanism to elect the CH. Now the whole network is divided as per the role of nodes some are CHs and cluster members (CMs). To enhance the energy efficiency, clustering approach can give direction to the sensor network which applies to various applications. So cluster analysis and energy efficiency both are co-related to improve the network lifetime. This research work is helpful to analyze the cluster formation and usage of energy efficiently. This approach is quad clustering which is used to form a single cluster into four clusters. Later the cluster head selection process takes place to make transmission easy with less usage of energy. This work shows the performance evaluation of clusters and the energy consumption of the network. The comparative phase between single and quad clusters considered the following parameters such as distance, number of nodes distribution, and energy usage. Keywords Cluster analysis · Energy consumption · Quad clustering · Node deployment · Distance coverage

B. Kumar (B) · S. Kumar · H. S. Negi Graphic Era Deemed to be University, Dehradun, India e-mail: [email protected] A. Kumar Shri Ram Group of Colleges, Muzaffarnagar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_17

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1 Introduction Due to their complicated, multifarious requirements that frequently reveal inherent tradeoffs, WSNs have recently achieved significant roles in a variety of applications and caught the interest of researchers [1]. Sensor nodes connected via ad hoc and selfconfiguring connections make up a wireless sensor network. Each node’s primary duty is environment monitoring [2] using the onboard sensors, though it may also be able to serve as a relay or data fusion node [3, 4]. The effective use of the stored energy is one of the strict requirements of these nodes [5]. For effective node energy management in WSNs utilizing various clustering strategies, several algorithms have been developed. Each coordinator (cluster head) in a WSN cluster is in charge of gathering data from the nodes and transmitting [6] it to the sink (base station). The sensor nodes are divided into various groups via a clustering technique. Each cluster is made up of a central cluster called the CH and several CMs. Numerous benefits of the clustering approach include scalability, enhanced network lifetime, and balanced energy usage. Numerous descriptions and definitions of the advantages and categories of the clustering approach have been published [7]. However, the clustering protocols [8, 9] do have some drawbacks, such as the transmission of data to the sink, which can occasionally increase the amount of energy consumed by WSNs since CH will need to travel the greatest distance to deliver data to BS. To conserve energy, multi-hop communication is typically used. Figure 1 shows how sensor nodes spread in the area and send data to respective cluster head and CH aggregates the collected data sent to the BS in the clustering approach. The black circle is represented CH and the hollow circle represented CMs. This paper describes the sections, where related work, proposed work, performance comparison, conclusion, and future work are listed below. Fig. 1 Sensor network architecture with cluster head

BS

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2 Literature Review Various clustering protocols like LEACH [10], HEED [11], PEGASIS [12], SEP [13], LEACH-E [14], DEEC [15] many more. Summary of related research papers are mentioned in Table 1. Various approaches have been proposed to enhance the energy level of sensor network. Above literature review gives the research directions to work on energy efficiency of the network using clustering technique. Clustering improves the performance level of each node to utilize the minimum energy during transmission [24]. Machine learning [25] is also helpful to form the clusters and selection procedure for CH. Later, the single cluster can also breaks into quad clusters. Table 1 Research paper details Author’s details

Objective

Description

Weaknesses

Zeb et al. [16]

Focused on performance and taxonomy of clustering analysis for WSNs

It works for cluster formation, complexity, communication, and management

Need clustering scheme for low cost, scalable, and robust

Aziz Mahboub et al. [17]

To improve the energy Node deployment is Mostly energy level of sensor network done by K-means consumption in clustering algorithm to transmission phase optimized the energy of sensor networks

Asha et al. [18]

To achieve the energy efficient approach for WSNs

Singh et al. [19]

Address the challenges Solve the multi-hop To work scalability of mobile sensor problem to improve the evaluations for speed network throughput and network sizes

Basavaraj et al. [20]

New energy efficient approach for cross layer sensor network

Compared with LEACH and later version protocols

Other parameters also be considered to select the CH

Rajkumar et al. [21]

Proposed enhanced energy approach for nodes

Minimum route cost is calculated

Cluster formation can also extend

Chen et al. [22]

Approach for Better localization Multi-level clustering clustering and centered reconstructs to detect can also implement on genetic algorithm location of unidentified nodes

Yan et al. [23]

This paper focused on clustering for energy efficiency using game theory

Find the dead and alive More enhanced nodes, packets sent approach can also be along with throughput a research space

Sensor nodes play a role as player

Adopt cooperative strategies

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3 Quad Clustering Quad clustering is an approach that splits the whole network into quadrants. To enhance the energy level of a sensor network, the whole sensor network divides into four small networks. A single network can be worked as a single cluster where CH is wholly responsible to collect the data from CMs. But in that case, energy consumption is very high. To increase the energy level of each node, a single cluster can be split into four clusters (sub-clusters). So that, each sub-clusters can have its CH. Quad clustering prolongs the network life cycle. Various works have been done to achieve quad clustering in a sensor network to improve the energy efficiency of nodes [26, 27].

3.1 Quad Clusters Formation In [28], the single cluster formation is done, later on, the expansion of clustering can proceed with four clusters formed quadrants. Quad-clustering implementation is done in [26, 27] for energy efficiency in Fig. 2 along with 200 nodes for 200 by 200 m area coverage [27]. The whole window is divided into four clusters. In the simulation, red point represented as centroid or BS station location and green node denoted as CH node, CMs are denoted by blue nodes. Centroid of each quadrant is represented by orange point. To calculate the distance between two nodes by using Eq. 1. / )2 ( Distance = (x2 − x1 )2 + y2 − y1

Fig. 2 Node deployment of 200 nodes in 200 by 200 m area [26]

(1)

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whereas, BS and each node position is at (x1 , y1 ) and (x2 , y2 ) co-ordinate.

3.2 Energy Formula Each node consumes the energy to tranmsit k bit data. Below is the formula of transmission mentioned in Eqs. 2 and 3 where distance is represented as d. ETx (k, d) = Eelec ∗ k + εfs ∗ k ∗ d2 , d < dthres

(2)

ETx (k, d) = Eelec ∗ k + εamp ∗ k ∗ d4 , d > dthres

(3)

where distance threshold is dthres =

/

εfs εamp

Receiving energy consumption for k bit data represented in Eq. 4. ERx (k) = Eelec ∗ k

(4)

4 Quad Clusters Analysis and Performance Evaluation 4.1 Simulation Environment Python is used to simulate node range between 200 and 500 for the 200 by 200 m for sensor network area. Table 2 has various parameters usage available to calculate the energy level. Table 2 Parameters

Parameters

Value

Sensor area (in meters)

(200, 200)

Nodes range

200–500

Eelec

50 nj/bit

Efs

10 pJ/bit/m2

Eamp

0.0013 pJ/bit/m4

Dthres

87 m

Eda

5 nj/bit/signal

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Fig. 3 Quad clusters with distance coverage for 200 nodes

4.2 Cluster Analysis K-means clustering [29] is used to form clusters in the sensor network. In this simulation, a BS is at the centroid of the sensor area, which is calculated by the K-means clustering approach. Quad cluster distance coverage by each cluster is shown in Fig. 3. As cluster 1 has 55 nodes, cluster 2 has 41 nodes, and clusters 3 and 4 both have 52 nodes, where the total number of nodes is 200 nodes. The same is for 250, 300, 350, 400, 450, and 500 nodes, which also divide into sub-cluster nodes. Each cluster has its own coverage distance. The first and fourth cluster distances of coverage are 4000 m above the ground. Where the second cluster is located nearby at 3000 m, and the third cluster is located above 3500 m. Figure 4 shows the number of nodes where the range is between 200 and 500 for the 200 by-200 m area, balanced clusters with their allotted nodes, where CHs and CMs are there to perform the transmission with aggregation. For 250 node distribution for quad clusters, 2, 3, and 4 are 70, 51, 61, and 68. For 300 node distribution in quad clusters, clusters 1, 2, 3, and 4 allotted nodes are 72, 82, 75, and 71. Clusters 1, 2, 3, and 4 allotted nodes are 90, 95, 80, and 85 for 350 nodes, and the same as for 400, 450, and 500 nodes. Sensor network distance coverage of quad cluster nodes for 200 to 500 nodes is shown in Fig. 5. The total distance covered by all nodes in each cluster is used to find out the energy consumption. In the comparison of a single and quad cluster, two parameters are considered: one is distance, and the other is energy consumption. Because the shortest distance is covered, the least amount of energy is consumed. Sensor network distance coverage of quad cluster nodes for 200–500 nodes is shown in Fig. 6. As distance d is mentioned in the energy calculation as per the condition. If the distance can be minimized to improve the residual energy of each node. When compared to quad clusters, the distance covered by a single cluster is very large. With concern to energy usage comparison shown in both cases of single versus quad clusters, so the performance output of quad clusters is better than the single cluster shown in Fig. 7. The quad

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Fig. 4 Cluster wise number of nodes for 200–500 nodes

Fig. 5 Cluster wise distance coverage for 200–500 nodes

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clustering approach is beneficial to save the energy of the whole sensor network, which may lead to an increase in the lifetime of transmission.

Fig. 6 Distance coverage of 200–500 nodes

Fig. 7 Energy Consumption of 200–500 nodes

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5 Conclusion Many applications of WSN focus on the energy consumption of sensor nodes, which may have an impact on transmission. The residual energy value should be high to improve the network life. Clustering is a method of extending the ability to maintain thousands of network nodes. The sensor network’s key features will be cluster analysis and performance evaluation of energy consumption based on distance and energy. Quad clustering is the process of splitting the whole network into four partitions on the basis of the centroid. Quad clustering is implemented by K-means algorithm, while the centroid position is also calculated. In this paper, an analysis of quad clusters and an evaluation of their energy consumption are considered. The range of nodes is 200–500, and the sensor area is 200 by 200 m. The node deployment is done using coordinate positions with x and y values. The implementation part of this paper shows the distance coverage, allotted nodes to clusters, and energy usage between single vs quad clusters. The results of this work show that quad clustering outperforms single clustering when compared to direct transmission. Quad clustering produces outstanding results in terms of distance coverage and a 20–30% increase in the energy of the sensor network. In the future, more clusters can also be formed to extend quad clustering. The other parameters, such as throughput, overhead, and scalability, can also be considered to work with real-time applications.

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Artificial Intelligence in Gaming Ritik Verma, Alka Chaudhary, Deepa Gupta, and Anil Kumar

Abstract Artificial Intelligence in games is mainly used to control/generate the behavior or the actions of the Non-Player Character (NPC) to a Human Being. AI is a strong selling point of Commercial Games (Video Games). Since games are associated with entertainment, but there are many serious applications of games/ gaming such as in medical field, military field. Many other games such as racing game, shooting games they all have different component of AI. This paper presents the usage of AI in gaming and shows their impact in different fields. Keywords Artificial Intelligence · Game · Galvanic skin response etc.

1 Introduction The AI in the gaming software is the effort going beyond the scripted line of codes, action and scripted interactions, however it is really complex in the arena of actual interactive system they are adaptive, responsive and intelligent, such type of system learns about the player during the gameplay and adapt their behavior, action, and interaction beyond the pre-programmed provided by the game developers, and it develop and provide player the richer experience and a better gameplaying software is the effort going beyond the scripted line of codes, action and scripted interactions, code set however it is really complex in the arena of actual interactive system. R. Verma · A. Chaudhary (B) · D. Gupta Amity Institute of Information Technology, Amity University Noida, Noida, Uttar Pradesh, India e-mail: [email protected] R. Verma e-mail: [email protected] D. Gupta e-mail: [email protected] A. Kumar School of Computing, DIT University, Dehradun, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_18

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They are adaptive, responsive, and intelligent, such type of system learns about the player during the gameplay and adapt their behavior, action, and interaction beyond the pre-programmed code set provided by the game developers, and it develop and provide the player the richer experience and a better gameplay [1, 2]. Artificial Intelligence or Machine Intelligence is intelligence shown by Machine not just computer but other devices such as Smartphones, Refrigerator, etc., in contrast to human intelligence. With the help of AI sometimes our system does not require human input. Computer games are the state of art that recreates the real-life environment/world with a great level of detail. This type of world contains many of character such as allies or enemy that require a human level of intelligence to exhibit realistic (believable) behavior and action. Since there is enormous advancement in computer graphics, animation, video, and audio. The main function of AI in games is to enhance the user gameplay [3]. Let us consider this scenario in the fighting or racing games when user is against the computer and when the match or race begin and after playing for a long period of time user gets to notice the repetition in the action of character that is controlled by the computer, This is where the AI kick in after getting a certain amount of data on user gameplay now AI starts working on the action of the character, now the character which computer is handling will start using the new set of action other than the pre-programed set of action to make user experience better and make user play their game for long interval of time. The machine learning algorithms run on user gameplay data to enhance the game world character action and life to create the game world environment as real as realworld environment and make game more realistic and natural to the user. To do these task AI algorithm needs or provided large amount of data to have the proper reactions to specific situation. Therefore, with the help of AI algorithm it can allow non-player character (NPC) to define the behavior that can adapt to the different situation and individual player, therefore reducing the effort and codes in addressing all the action or behavior in the game these games are called adaptive games, i.e., games that can adapt themselves for unseen situation [4]. Adaptive games can reduce the effort in development, if a game can adapt itself then the developer requires less effort in programming all possible situations.

2 Artificial Intelligence Artificial intelligence (AI) is knowledge displayed by machines as opposed to the common understanding displayed by beings like humans. Man-made intelligence study has been referred to as the domain of shrewd specialists, which alludes to any framework that observes the environment and takes action to increase its likelihood of achieving its goals. Significant AI scientists now view AI as sound and behaving wisely, which doesn’t limit how insight might be described. They have now rejected this concept.

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Automated steering and competing at the highest level in crucial game frameworks are examples of artificial intelligence machines. The AI effect is a quirk that occurs when robots grow increasingly competent: tasks that are thought to need “knowledge” are usually removed from the definition of AI. For instance, optical person recognition, which has become a regular invention, is frequently excluded from items considered to be AI [5, 6].

3 Review of Literature [A] Repeated Stochastic Games based on Adaptive Learning: Multiagent learning algorithms that function in complex contexts alongside other learning algorithms are becoming more and more necessary. For simple games, many multiagent learning algorithms have been created; however, there are fewer algorithms for complicated situations (e.g., stochastic games). The challenge is in needing to learn a model of the adversaries in addition to the environment. When the adversaries are also learning, this becomes even more challenging. An agent that can pick up on and quickly adjust to changes in adversary behavior will have an advantage. Because of the extensive state space, stochastic games are challenging to play well. In reinforcement learning, state abstractions have been employed successfully to deal with expansive state spaces and environmental changes. In huge games like Poker, such as game abstraction has also been utilized to develop efficient strategies. But the majority of abstraction techniques are game-specific. Recently, Sandholm et al. presented a comprehensive technique for stochastic games and argued the need for such a universal lossy abstraction. In any general stochastic game, FALSG uses a general game abstraction strategy that enables it to concentrate on learning quickly and adjusting to opponent changes [7]. Fast adaptive learning is based on practical applications where an agent only interacts with other agents occasionally. The convergence to an equilibrium strategy is a common topic of multiagent learning methods. However, the majority of real-world domains do not have the luxury of allowing for such a huge number of interactions (e.g., auction bidding, search and rescue operations, and stock market investing). [B] Adaptive Gaming Using Biofeedback: The amount of competence attained over time and prior playing experience both affect a player’s performance during a game. Imagine playing a game of Pong with one experienced player and the other novice. The game will almost certainly be won by the more experienced player who won’t be challenged by the other player. Players may also reach a point where they are no longer inspired or challenged. Biofeedback can be used to make games more difficult or to give players of varied skill levels an equivalent gaming experience. Players experience various emotions (e.g., boredom and frustration). A player’s heart rate and skin conductance, or galvanic skin response (GSR), can reveal information about their emotional state. For instance, a person is more euphoric when their heart rate is higher, and they are under more stress when their GSR is higher. Because

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the sensors are small and the costs are modest, these two input methods are reasonably simple to utilize in video games. Other video games made use of biofeedback. Biometric input has taken the place of traditional input in some games. For instance, in “Relax-to-win,” a player can influence a dragon’s speed by relaxing, whereas with the Atari Mind link controller, muscle activity controls the game. Biofeedback is utilized in “Bio feed the zombies” to adjust the game’s environment (i.e., make it scarier or less scary) based on the player’s physiological state. The overall goal of this study was to determine whether adaptive gameplay with biometrics may enhance user experience and performance in Pong. [C] AI-based Adaptive Computer Games: Modern video games accurately simulate real-world settings with a startling level of detail. Many characters (friends or foes) that require human-level intellect and behave realistically typically fill these areas. However, even though computer graphics, animation, and audio for games have made huge strides, most of them still use extremely simple artificial intelligence (AI) algorithms. Because of the AI’s basic design, when the game and the characters contained within it behave in an unconscionable way, the mood the game has worked so hard to build can be destroyed. The development of deeper experiences. These methods can lessen the amount of effort needed to build a complicated game’s many possible outcomes by allowing non-AI professionals to specify behaviors for characters that can subsequently be modified to fit various scenarios and players. We are particularly interested in adaptive games, or games that may modify their behavior in response to unforeseen circumstances. For a more thorough examination of adaptability in video games [8]. [D] Adaptive Companions in FPS Games: The player’s interaction with team members or companion NPCs in first-person shooters (FPS) and third-person shooters (TPS) is closely tied to the gameplay, challenge, storyline, and experience. The companion’s objective is to assist the player in completing in-game objectives in order to simulate the effects of real-world cooperative gameplay. But even in AAA games, we frequently find that companions are extremely superficial; they may coexist with the player, but they typically fail to correctly interact, diminishing their worth to gamers and interfering with immersion. This condition presents interesting challenges and concerns. Fundamentally, these issues result from the companion’s ignorance of the game’s universe and the player’s dynamic, ever-evolving experience. The companion is given a fundamental understanding of the player’s experience, and it makes use of that information to adjust its behavior. We created a prototype TPS game with a simple level where the player is supposed to interact with this companion in order to verify our adaptive companion. Additionally, an Artificial Intelligence (AI) was added to simulate a player for the sake of testing and calculating player-companion metrics. We demonstrate that, compared to a traditional gameindustry approach to NPC creation, our approach to companion adaptivity gives the companion superior control over the player’s gaming experience. Additionally, we show how behavioral adaptivity and difficulty adaptivity are similar and how, in situations where DDA is weak, behavioral adaptivity can be included into a story [9].

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We compare our adaptive design against a companion AI drawn from the wellknown Skyrim game, as well as to conventional attribute-scaling approaches to DDA, using a straightforward but representative game setting created in Unity. Our method yields quantifiable gains, demonstrating that complex adaptivity can be employed as a complementary strategy to DDA. [E] Evaluating AI-Based Games through Retellings: The goal of introducing procedural content creation and artificial intelligence into games is to offer a larger range of more substantially different experiences from player to player. Another, related claim is that PCG and AI will allow games to react to user interaction more profoundly and meaningfully. However, current evaluation techniques don’t accomplish much to bring this aspect of player experience to light. Because of this, this aspect of player experience frequently gets overlooked in our evaluations of AI- and PCG-based games. Retellings differ from play traces, which try to capture what occurred during play in an objective manner, in some respects and are comparable to them in others. We suggest using retellings as a type of play trace with a subjective foundation for research. We claim that during gameplay, gamers frequently conjure up or make up narratives that go beyond what is technically depicted in the game. They may give events or details that, from the perspective of gameplay, appear to be unimportant an unexpected amount of weight, extrapolate the implications of those events or details in ways that the game’s creators did not intend, and generally bring their own creativity and subjectivity to the process of narrating their play experiences [9]. The retellings that players create when they reflect on their play experiences to share or retell them to others may then capture aspects of experience that are missing from even the most thorough play trace. Additionally, whether and how a game may be narratively understood by players may reveal information about its overall plausibility [10–12]. [F] Expressive AI: Games and Artificial Intelligence: Game designers have been calling for a language of design for a while now, pointing out that there isn’t currently a common vocabulary for discussing the design of already-existing games or formulating ideas for brand-new games. Design rules, which provide imperative guidance in particular design situations, are closely related to design patterns, which identify and describe design elements. Both concepts are typically expressed at an abstraction level above the game’s implementation details, focusing solely on the connections between visual components, game rules, and player interaction. A design language may be applied in a variety of contexts by abstracting away from implementation specifics; at its most extreme, talking and thinking about game design may be abstracted totally from game implementation [13]. The term “game AI” merges several gameplay mechanics around the issue of “intelligent” conduct, or activity that the player can interpret as being produced by an intelligence with its own desires. This behavior appears to be a reaction to the player’s actions, which are significantly tied to the player’s activities [2].

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4 Comparison A compression table that summarizes each paper reviewed is given below: Paper title

Journal

Author

Method used

Conclusion

Fast Adaptive Learning in Repeated Stochastic Games

Jacob Crandall Department of Electrical Engineering and Computer Science Masdar Institute of Science and Technology Abu Dhabi, United Arab Emirates

Mohamed Elidrisi, Nicholas Johnson, Maria Gini

• Meta-game model • Predictive model • Reasoning model

The debut of FAL-SG, a multiagent learning system that uses game abstraction to enable quick learning and opponent adaptation in repeated stochastic games. According to our investigation, the number of inaccurate predictions made by FAL-SG is bounded and the abstraction accurately depicts the underlying game

Dirrik H.G. Emmen, Georgios Lampropoulos

• Heart rate • Global Software Resources • Verbal comments and nonverbal reactions of the players

Without taking into account the players’ baseline heart rate and GSR, the physiological state of the participants was divided into various ranges. Individuals have different baselines. This implies that two people who are experiencing the same emotional state physically differ from one another. In order to determine if heart rate and GSR are elevated (showing stress) or not, base levels should be taken into consideration

Biofeedback-based Leiden BioPong: Adaptive University Gaming Using Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands

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(continued) Paper title

Journal

Author

Method used

Conclusion

Artificial Intelligence for Adaptive Computer Games

Ashwin Ram, Santiago Ontan˜on, and Manish Mehta

Cognitive Computing Lab (CCL) College of Computing, Georgia Institute of Technology Atlanta, Georgia, USA

• Behavior Transformation System • Based Behavior Learning in Wargus

Modern computer games present the artificial intelligence community with a number of difficulties. It is difficult to create AI methods that can handle the complexity of computer games, but doing so could have significant effects on entertainment, training, and education

Adaptive Companions in FPS Games

Jonathan Tremblay, Clark Zeebrugge

School of Computer Science McGill University, Montréal Québec, Canada

• Game Prototype • Base Companion • Adaptive companion • Metrics

Companion agents are frequently used in contemporary computer games to aid players in completing objectives and building richer narratives. However, this introduction also introduces additional issues, such as the companion ruining the player’s experience by disobeying the player’s goals and playing manner (continued)

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(continued) Paper title

Journal

Author

Method used

Conclusion

Uses of Retellings for Evaluating AI-Based Games

Max Kreminski, Ben Samuel, Edward Melcer, Noah Wardrip-Fruin

University of California, Santa Cruz, University of New Orleans

• Player perspective • In-world perspective • Narrator perspective

For some games, the presence of a significant corpus of “naturally occurring” retellings may be taken as a sign that the game is successful. We also draw the conclusion that examining the content of retellings can provide more light on the game elements that encourage player storytelling

Expressive AI for Games and Artificial Intelligence

Michael Mateas

The Georgia • Design AI Institute of • Game AI Technology • Behavior tree School of Literature, Communication and Culture and College of Computing 686 Cherry Street Atlanta, GA 30,332-0165 USA

Whatever the AI’s specific task, whether it be to kill the player

5 Conclusion Artificial Intelligence technique that can manage the density of computer games is a major challenge yet can possibly have a major effect in a few areas including entertainment, education, and training. In this the primary goal is to create Artificial Intelligence strategies that can facilitate the exertion of joining AI in computer games to make them progressively adaptive and speaking to people, these games are called adaptive games. The main conclusion can be drawn that drama management methods are relevant to real-time games and drama management for the most part improves player experience. I believe that that Artificial Intelligence in computer gaming will be more efficient and it will give the player a good experience of games.

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References 1. Aarseth EJ (1997) Cybertext: perspectives on ergodic literature. The Johns Hopkins University Press, Baltimore 2. Church D (1999) Formal abstract design tools. Gamasutra.com. http://www.gamasutra.com/ features/19990716/design_tools_01.htm 3. Classicgaming.com. http://www.classicgaming.com/sngp/games/pac/pacman_review04.htm. 4. Dennett D (1989) The intentional stance. MIT Press, Cambridge, MA 5. Dobson W (ed) (2000) Papers from the 2000 AAAI Spring symposium on artificial intelligence and interactive entertainment. AAAI Press, Menlo Park, CA 6. https://www.imedpub.com/articles/a-brief-study-on-manmade-artificialintelligence.php?aid= 46629 7. Eskelinen M (2001) Towards computer game studies. In: Proceedings of SIGGRAPH 2001, art gallery, art and culture papers, pp 83–87 8. Karlgren F, El-Nasr M (2002) Papers from the 2002 AAAI spring symposium on artificial intelligence and interactive entertainment. AAAI Press, Menlo Park, CA 9. Frasca G (2001) Videogames of the oppressed: videogames as a means for critical thinking and debate.Masters thesis, interactive design and technology program, Georgia Institute of Technology. www.ludology.org 10. Chaudhary A (2019) Mamdani and SUGENO fuzzy inference systems’ comparison for detection of packet dropping attack in mobile ad hoc networks. In: Emerging technologies in data mining and information security. Springer, Singapore, pp 805–811 11. Yadav H, Chaudhary A, Rana A (2020) Ultra low power SRAM cell for high speed applications using 90 nm CMOS technology. In: 2020 8th international conference on reliability, Infocom technologies and optimization (Trends and Future Directions) (ICRITO). IEEE 12. Chaudhary A, Kumar A, Tiwari VN (2015) A cooperative intrusion detection system for sleep deprivation attack using neuro-fuzzy classifier in mobile ad hoc networks. In: Computational intelligence in data mining, vol 2. Springer, New Delhi, pp 345–353 13. Chaudhary A, Kumar A, Tiwari VN (2014) A reliable solution against packet dropping attack due to malicious nodes using fuzzy logic in MANETs. In: 2014 international conference on reliability optimization and information technology (ICROIT). IEEE

Analysis of Pulmonary Fibrosis Progression Using Machine Learning Approaches Shivani Agarwal, Avdhesh Gupta, Vishan Kumar Gupta, Akanksha Shukla, Anjali Sardana, and Priyank Pandey

Abstract Pulmonary fibrosis is a progressive lung illness, it usually gets worse over time as the disease progresses. Scarring develops in the lungs as a result of this condition over time. As a direct consequence of this, people have trouble breathing. Toxic elements are one of the leading contributors to the development of pulmonary fibrosis. These elements include coal dust, asbestos fibers, silica dust, hard metal dusts, and many others. On the other hand, in the overwhelming majority of instances, the physician is unable to determine the precise reason why this sickness occurs. Idiopathic pulmonary fibrosis is the name given to this ailment because it cannot be attributed to any specific cause. This project’s objective is to evaluate the performance of several different machine learning models by making predictions regarding the final forced volume capacity measurements and a confidence value for each patient. On the basis of a CT scan of the patient’s lungs, it may be utilized on any computer to make an accurate prognosis of the poor condition of the patient’s lungs in relation to their ability to operate. A spirometer, which measures the forced vital capacity of the lungs, is utilized in the assessment of pulmonary function (FVC). In the future, it is hoped that pulmonary fibrosis can be identified at an earlier stage. The paradigm of machine learning is helping to improve the effectiveness of the use of human resources while simultaneously lowering costs associated with the social and medical repercussions of this life-threatening illness. Keywords Pulmonary · Fibrosis · EfficientNet · Forced vital capacity · Machine learning

S. Agarwal · A. Gupta · A. Shukla Ajay Kumar Garg Engineering College, Ghaziabad, India V. K. Gupta (B) · P. Pandey Graphic Era Deemed to Be University, Dehradun, India e-mail: [email protected] A. Sardana ABES Engineering College, Ghaziabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_19

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1 Introduction The phrase “pulmonary” refers to the lung, and the term “fibrosis” refers to scar tissue, which is analogous to scars that may appear on your skin as a result of an old wound or surgery. It is possible to have shortness of breath even when at rest. Pulmonary fibrosis (PF) can be simplified to its most fundamental form, which is scarring in the lungs. Scar tissue has the potential to harm normal lung tissue over time, which will make it more difficult for oxygen to reach the bloodstream. Experiencing shortness of breath is a common symptom of low oxygen levels and can be exacerbated by activities such as walking and other forms of exercise. The prognosis of an illness is an assessment of how the condition is expected to progress in the future. People frequently use this term to refer to a person’s life expectancy, which is another way of saying how long it is expected that a person will live. However, prognosis can also be used to refer to the likelihood that a disease will be cured as well as the outlook for functional recovery. It is not possible to entirely recover from this condition once the lungs have been damaged. Nevertheless, early discovery and accurate diagnosis might be helpful in bringing this disease under control [1]. This includes the likelihood of being able to return to work and participate in recreational activities as well as the expected level of assistance required to perform activities of daily living. Spirometry is a test that evaluates how much air you are able to forcefully evacuate from your lungs after taking the deepest breath you can possibly take. The forced vital capacity (FVC) can also be used to aid medical professionals in identifying the course of lung disease as well as the effectiveness of treatment [2]. It is possible to evaluate the patient’s FVC volume in relation to the average FVC volume for people of the same age, gender, height, and weight. If it is possible to do so, the patient’s FVC can be compared to the patient’s own previous FVC values in order to ascertain whether or not the pulmonary condition is getting worse or whether or not the treatment is having an effect on the patient’s lung function. Another way to express FVC is as a percentage of the predicted value for FVC. The usual range for an adult’s FVC is somewhere between 3.0 and 5.0 L [3].

1.1 Basic Details In order to evaluate this piece of writing, a customized variation of the Laplace Log Likelihood was utilized. In medical applications, it is helpful to evaluate the confidence that a model has in the decisions that it makes. As a consequence of this, the metric is supposed to be able to measure both how accurate each forecast is and how confident it is. For each authentic FVC measurement, you will make a prediction for the FVC, as well as a confidence measure (the standard deviation). The metric is computed as: Confidence values smaller than 70 are clipped. σclipped = max (σ, 70)

(1)

Analysis of Pulmonary Fibrosis Progression Using Machine Learning …

I ) (I Δ = min IFVCtrue − FVCpredicted I, 1000

215

(2)

Errors greater than 1000 are also clipped in order to avoid large errors. The metric is defined as √ √ 2Δ metric = − − ln( 2σ clipped) (3) σ clipped The top scorers on each test are determined using around 15% of the total data. Because the remaining 85% will be used to determine the final results, the final standings could end up being different [3].

1.2 How Pulmonary Fibrosis is Diagnosed? There are various terminologies and a collaborative effort amongst specialized teams allows for the accurate diagnosis of pulmonary fibrosis using a variety of tests these are: 1. CHEST X-RAY: X-rays are used in the process of creating a flat, twodimensional image of the lining of your chest through a procedure known as a chest X-ray. An X-ray is a type of radiation that travels through the body and captures images via a process called “scanning.” The illustration shows that all aspects of the body that are similar to them are white. Solid tissues, such as the liver and the heart, are visible and appear white in color. As a result, the lungs get darker, which makes it easier for X-rays to penetrate them [4]. 2. CT-SCAN: The abbreviation CT is used to refer to computed tomography. Through the use of X-rays, it creates a model of the interior of your body that is three-dimensional. This provides a detailed view of your organs, including your blood vessels, lungs, and other organs. In individuals who have lung cancer, a CT scan may be used to monitor the response of the lungs to medicine or to track an abnormality in the lungs itself [5]. 3. PET-SCAN: The abbreviation “PET” stands for “positron emission tomography.” The amount of cell activity everywhere throughout your body can be measured with the use of this particular type of scan. The scan will produce extremely accurate and detailed three-dimensional images of the interior of your body. The PET scan is used to determine how quickly the energy that is contained in this chemical is used up. The portion of the scan that is more illuminated suggests that the cells are expending a greater amount of energy. These brighter patches could be detected if there is inflammation, an infection, or malignancy in the area [6].

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1.3 Different Procedures for Finding the Lung Tissue There are some procedures and their uses as follows: 1. Bronchoscopy: A bronchoscopy is a procedure that is designed to examine your lungs in order to search for any abnormalities and to collect lung samples for further examination. It makes use of a fiber-optic bronchoscope, which is a tube that is both flexible and thin. It includes a light and a small camera attached to one end of it so that the person who is performing the inspection can observe your airways. The tube is passed down into the lungs after being put into the windpipe of the patient through the mouth or nose. In order to complete the examination, it is necessary to collect some lung cells and then deliver them to the laboratory. The results of an analysis can disclose the presence of any inflammation and any infections that may be present. It is possible that clearing up your bronchial passages will make it easier to discover cancer cells [3]. 2. VATS: In order to detect and treat a wide range of chest-related conditions, a specific kind of operation known as video-assisted thoracoscopic surgery (VATS) is frequently utilized. A thoracoscope, which is essentially a specialized video camera, is used for the procedure. It falls under the purview of surgery that requires only a minimal amount of intervention. This suggests that there are less cuts (incisions) made compared to the traditional method of open surgery. A common rationale for a VATS procedure is the removal of a part of the lung due to the presence of cancer [7]. 3. Spirometry: Spirometry is a simple test that evaluates how much air a person is able to forcefully expel in one breath in order to assist in the diagnosis and monitoring of certain lung conditions. The examination is carried out with the assistance of a spirometer, which is a portable device that consists of a mouthpiece and a cable connecting the two [8].

2 Literature Review The recognition of hand gestures has been the subject of a substantial amount of research employing a variety of methodologies. In this work, the researcher examines the differences between a variety of machine learning models and explains how well these models perform when it comes to evaluating Pulmonary Progression. This prediction analysis has the potential to be applied in the medical field to assist patients by analyzing the condition of their lungs using information obtained from CT scans and other sources for the purpose of providing better treatment or diagnosis [9]. This would allow for treatment to begin as quickly as possible. As a consequence of this, the strategies of machine learning are able to provide medical practitioners with assistance in better determining and analyzing the prognoses of patients when they are initially diagnosed with IPF [1]. The authors [2] established in their research that persistent is produced by structural and inflammatory cells and that it is uncontrolled during fibrotic responses in both mice and humans. These findings were

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presented in the context of the authors’ previous work. They indicate direct effects of persisting on lung mesenchymal cells, including as induction of extracellular matrix, enhanced proliferation, and encouragement of wound healing. In addition, plasma periostin has the potential to be demonstrated as a helpful biomarker that can predict the early declaration of disease. Furthermore, periostin and TGF-beta have been shown to function as a positive feedback loop that can interpose in patients. Patients suffering from diffuse lung disease as well as other conditions for which there are no definitive quality-of-care criteria. This study provided evidence that proper research can be designed to answer questions about the disease in individuals who have received treatment as well as patients who have not received treatment. The significant question, in particular the challenging issue of deterioration prevention, will involve research involving a large number of patients, seen in specialized centers with an interest in diffuse lung illnesses, as well as a globally coordinated strategy. These are both requirements. These concerns have been disregarded for a far longer time than is appropriate. We need a strategy that is significantly more logical, and we now have the opportunity to rise to the occasion [4]. The primary focus of this research is on the application of functional indicators to identify patients who are at a greater risk of falling. Another work on COVID-19 reveals that machine learning techniques do not take into account any supplementary data from varying model or template structures. Evaluation is done on how accurate each model is. The author took into consideration using random forest as a prediction model for our many different scenarios because its performance is superior to that of other machine learning methods. For the purpose of determining the random forest model’s level of reliability, the K-fold cross-validation method was applied [10]. The authors of this study [5] presented the analytical modules region unequal treatment, tissue classification, object segmentation, and object classification. They came to the conclusion that the classification of tissues, the differentiation of regions, and the count of mast cells are all excellent predictors of bleomycin-induced pulmonary fibrosis. According to the data presented in the research [6], a computer-based quantitative CT technique like CALIPER may have various key applications in the evaluation of patients who have idiopathic pulmonary fibrosis (IPF). CALIPER’s greater sensitivity in evaluating ILD extent has the potential to improve knowledge of the natural history of IPF by improving the accuracy of identifying serial change. This improvement is possible since CALIPER’s improved sensitivity compares favorably to visual grading. The examination of CT scans by a computer has a number of possible uses in clinical trials. The research also came to the conclusion that in the analysis of patients with IPF, a novel Caliper CT parameter known as pulmonary volume vessel that does not have a visual scoring equivalent has the possibility of having a CT feature and needs more investigation. The FVC-Net model, which used metadata and CT scan images to predict FVC and was evaluated using the modified Laplace Log-Likelihood score, was proposed in the publication [3]. The paper also included an evaluation of the model. FVC-performance Net’s was noticeably superior than that of EN, EQR, LR, and RF, as well as to that of other models that were published in the research literature. When evaluated by the proposed deep learning algorithm, high-resolution CT, which according to the proposed method, provides a low-cost, fast, and accurate way

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to detect a decline in lung function in a patient suffering from pulmonary fibrosis, is a method that can detect this decline in lung function. This technology could be highly valuable in facilities that lack experience in thoracic imaging, which would make the diagnosis process simpler for medical professionals. Pirfenidone usage in patients with PPF is related to a statistically significant decrease in disease progression and protection of lung function, as stated by the authors [7]. However, there is very little assurance in the estimated impacts because there are limitations in the evidence that is currently available. According to the findings of this study [8], a significant factor in determining the prognosis of idiopathic pulmonary fibrosis is the rate at which the disease progresses (DP). (IPF). The objective of this study was to investigate potential serum biomarkers for the diagnosis of DP in IPF patients at baseline [11]. Fibrosis-Net is able to not only predict FVC with greater accuracy than existing methods, but it is also able to do so in a more trustworthy and validated manner that makes use of clinically relevant visual indicators within a pulmonary fibrosis patient’s CT image, as stated in a paper [12]. This method leverages clinically relevant visual indicators that are present in a pulmonary fibrosis patient’s CT image. This method is not only an estimation that is ready for production for clinical use, but it also lays the groundwork for additional review and analysis using explain ability techniques such as GSInquire. An algorithm that is presented by the author [13] is one that is constructed using a mix of DNN, GBDT, NGBoost, and ElasticNet. With the help of this method, you will not need to access the findings of the patient’s chest computed tomography in order to make a prediction using tabular patient data. Throughout the course of the simulation, the algorithm displayed the highest levels of performance compared to the other approaches that were taken into consideration [14]. The author believes that quintile regression offers more accurate results in crossvalidation sets than elastic net does. Because of this prediction, the algorithm is able to identify various elements or stages of IPF, which will result in improved medication and overall health care for the patient. The challenges that can be encountered when attempting to identify honeycombing are also a component of this study. It turns out that traction bronchiectasis is a powerful tool in terms of being able to anticipate the patient’s prognosis [15]. Pulmonary fibrosis is a significant problem in today’s world, as indicated by the results of a survey [16]. To accurately anticipate early detection, which might potentially save the lives of a great number of people, an excellent computerized system is required. Using this approach, it will be easier for doctors and other medical professionals to diagnose patients and monitor their development [17]. These automated systems are primarily concerned with selecting relevant features and making accurate predictions. Selecting the appropriate characteristic can be of assistance in the process of diagnosing and monitoring pulmonary fibrosis. The accuracy of the results produced by search algorithms that utilize machine learning techniques and choose features to search for produces better outcomes [18]. If the treatment is successful, patients and their families will be able to learn their prognosis much sooner after they are initially diagnosed with the condition. This advancement in earlier detection may have a positive impact on the design of therapy trials and may hasten the clinical progression of new patients [19].

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3 Methodology We defined the OSIC Pulmonary dataset in this suggested model, which comprises Patient ID, weeks, FVC, Percentage, sex, and smoking status. This was done in this proposed model. This data set requires us to calculate the metrics and RMSE error value between the FVC actual value and the FVC projected value in order to identify the labels of division of patients into distinct classes, such as “Extremely smoker, never smoked, and Current Smoker.” We associate the patient’s chest CT scan from clinical information, which displays week-by-week data, in order to calculate how many weeks, the FVC value is high at whichever week it is high FVC with respect to the patient ID. This allows us to determine how many weeks the FVC value has been high. Define the process of prediction of fibrosis patients using this flow chart. For this prediction, first obtain the CT scan database from the Kaggle of patients. This database contains different CT scans of patients that are separated by patient ID. Next, use a random selection algorithm with a slicing approach to extract features from this database. Each patient’s scan consists of multiple slices, each of which reveals a different level of lung penetration. In order to accomplish this, we will first lower the high dimensionality of the data. This will allow us to simplify the process and cut costs. We utilize the Keras with nibabel package to perform pre-processing on the data obtained from CT scans. After you have corrected the orientation, rotate each of the volumes by ninety degrees. The values of the Hounsfield units are normalized so that they fall somewhere in the range of 0–1. We make adjustments to the height, width, and overall size. 20% of the slices were trimmed at the beginning and end because there is little dimension statistics in these slices, and two were taken from the middle and placed in CNN. This was accomplished by applying this model for dimension reduction before it was applied to CNN. We extended the CT slice that had been chosen at random in order to conform to the input criteria of the backbone CNN model, which were 1024 by 1024. In addition, in order to reduce the differences in organ strengths that are measured by individual sensors and to increase simulation divergence. Make your prediction of the slope value using linear priori using the week-by-week outcomes, then calculate the error using MSE.

3.1 Formulation of FVC Slope In this paper, Using the determined starting slope of the FVC values, we developed a novel formulation to forecast the slope of the FVCs as actuality. For the process of CT scans of n number of patients by IDs it is {CTi …CTn }. By using random selection method extract random features from the pool that is represented by slice S m from all the features S f . By use of CNN model extract features that is {f i …f n }.

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Now to represent the FVC slope value li with j week-wise patient id so it defined as W i j = li_a j + wbi

(4)

where Wij represents the FVC value of the patient week wise, li represents the slope value and Wb represents the base value and it is expanded with Σ

ai j W j + V b

(5)

In this work, proposed how CNN model is used for extract features from CT scans for better slope prediction. We focus on the features extractor and resizing the windows of CNN model so that the complexity reduces. Figure 1 shows the methodology of working. Fig. 1 Methodology

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4 Result A preliminary chest CT medical scan performed by MRI is included in the dataset together with the connected clinical data of a group of patients. There are a large number of patients’ identifiers included in the dataset, and each of them has a CT scan image that was taken on a specific week. These images have a variety of attributes, including the patient’s identifier, the week, their FVC value, their age, their gender, and whether or not they smoke. Figure 2 shows a glance of dataset of chest CT medical scanned images. Checking the patient’s FVC value using Bayesian linear regression at the beginning of each week to determine both the certain and uncertain FVC values is part of the procedure described below. The process begins with week 0 and progresses through an increasing number of weeks. There are now two different kinds of sets available: the first is a training set database, and the second is a set of test set datasets. These are put to use in order to anticipate the FVC value with a high degree of precision. By comparing the anticipated and actual values of FVC, we were able to calculate the mean absolute error, which came out to be 83.64 for this particular patient id. Table 1 shows the features and class of patients which is suffering with pulmonary fibrosis progression.

Fig. 2 Preliminary chest CT medical scan performed by MRI

Table 1 Features and class of patients suffering with pulmonary fibrosis SN

Patient

W

FVC

Percent

Age

Sex

Smoking status

0

ID00007637202177411956430

2

2178

52.04

50

Male

Ex-smoker

1

ID00007637202177411956430

3

2324

54.62

50

Male

Ex-smoker

2

ID00007637202177411956430

4

2145

51.81

50

Male

Ex-smoker

3

ID00007637202177411956430

5

2078

50.67

50

Male

Ex-smoker

4

ID00007637202177411956430

6

2267

52.06

50

Male

Ex-smoker

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Learning by transfer with the use of CNN is the most efficient method. The performance of CNN-based approaches is superior to that of conventional methods since these approaches can automatically learn and select features successfully. The Convolution Layer makes it possible for the one-of-a-kind feature to acquire specialized knowledge from the existing dataset. As a consequence of this, the one-of-a-kind characteristic plays an essential part in improving precision; it is essential to find a middle ground between a model’s capacity for presentation and overfitting. A network that is overly simplistic will be unable to learn from the data it is given, and as a result, it will not be precise enough. The model uses the FVC real value as well as the anticipated values in order to determine the error that exists between the two sets of data. This allows us to attain the highest possible level of accuracy in our predictions. Figure 3 represents classification of data using PieChart. This chart represents the number of smokers as ex-smoker, current smoker, and never smoked patients.

Fig. 3 Representation of classification of data using Pie-Chart

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5 Conclusion The predictive analysis presented in this paper has the potential to be utilized in the field of medicine to assist patients. This can be accomplished by analyzing the condition of the patients’ lungs using information obtained from CT scans and other sources in order to provide a more accurate diagnosis or treatment plan. As a consequence of this, the methods of machine learning are able to provide medical practitioners with assistance in better determining and analyzing the prognoses of patients when they are initially diagnosed with IPF. The best strategy or solution for assessing the progression of disease in persons who have IPF is to track changes in their FVC.

6 Future Scope Because daily spirometry is more sensitive than interval hospital-based spirometry when it comes to predicting future illness progression and survival, the forced vital capacity (FVC) measured at home may be used as an effectiveness goal to assist quicker early-phase clinical research. Importantly, only four subjects dropped out of the study during the first 90 days (two due to the rapidity with which their condition was deteriorating), and only one of them had fewer than 30 evaluable FVC values. This demonstrates that the research was carried out successfully. This demonstrates that a time limit of three months would be suitable for clinical trial participants as it would be instructive and acceptable to them. Home life is intertwined with monitoring the course of a life-limiting sickness that moves forward unstoppably.

References 1. Mandal S, Balas VE, Shaw RN, Ghosh A (2020) Prediction analysis of idiopathic pulmonary fibrosis progression from OSIC dataset. In: 2020 IEEE International conference on computing, power and communication technologies (GUCON). IEEE, pp 861–865 2. Naik PK, Bozyk PD, Bentley JK, Popova AP, Birch CM, Wilke CA (2012) COMET investigators, periostin promotes fibrosis and predicts progression in patients with idiopathic pulmonary fibrosis. Am J Physiol-Lung Cell Mol Physiol 303(12):L1046–L1056 3. Yadav A, Saxena R, Kumar A, Walia TS, Zaguia A, Kamal SM (2022) FVC-NET: an automated diagnosis of pulmonary fibrosis progression prediction using honeycombing and deep learning. Comput Intell Neurosc 4. Wells AU, Du Bois RM (1994) Prediction of disease progression in idiopathic pulmonary fibrosis. Eur Respir J 7(4):637–639 5. Stritt M, Bär R, Freyss J, Marrie J, Vezzali E, Weber E, Stalder A (2011) Supervised machine learning methods for quantification of pulmonary fibrosis. In: MDA, pp 24–37 6. Jacob J, Bartholmai BJ, Rajagopalan S, Kokosi M, Nair A, Karwoski R, Hansell DM (2016) Automated quantitative computed tomography versus visual computed tomography scoring in idiopathic pulmonary fibrosis. J Thorac Imaging 31(5):304–311

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7. Ghazipura M, Mammen MJ, Bissell BD, Macrea M, Herman DD, Hon SM, Hossain T (2022) Pirfenidone in progressive pulmonary fibrosis: a systematic review and meta-analysis. Ann Am Thoracic Soc 19(6):1030–1039 8. Hung C, Kim J, Cho HS, Kim HC (2022) Baseline serum Krebs von den Lungen-6 as a biomarker for the disease progression in idiopathic pulmonary fibrosis. Sci Rep 12(1):1–8 9. Bansal A, Kapil D, Anupriya, Agarwal S, Gupta VK (2022) Analysis and detection of various DDoS attacks on internet of things network. Int J Wirel Microw Technol (IJWMT) 12(3):18–32 10. Gupta VK, Gupta A, Kumar D, Sardana A (2021) Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model. Big Data Min Anal 4(2):116–123 11. Gupta VK, Rana PS (2021) Toxicity prediction of small drug molecules of aryl hydro carbon receptor using a proposed ensemble model. Turk J Electr Eng Co 24(4):2833–2849 12. Wong A, Lu J, Dorfman A, McInnis P, Famouri M, Manary D, Lynch M (2021) FibrosisNet: a tailored deep convolutional neural network design for prediction of pulmonary fibrosis progression from chest CT images. Front Artif Intell 4:764047 13. Glotov A, Lyakhov P (2021) Pulmonary fibrosis progression prognosis using machine learning. In: 2021 Ural symposium on biomedical engineering, radio electronics and information technology (USBEREIT). IEEE, pp 0327–0329 14. Gupta VK, Rana PS (2021) Ensemble technique for toxicity prediction of small drug molecules of the antioxidant response element signaling pathway. Comput J 64(7) 15. Imaging: how to recognize idiopathic pulmonary fibrosis. https://err.ersjournals.com/content/ 23/132/215 16. Pulmonary fibrosis progression dataset. https://www.kaggle.com/c/osic-pulmonary-fibrosisp rogression/discussion/165727. Accessed 22 Sept 2022 17. Christe A, Peters AA, Drakopoulos D, Heverhagen JT, Geiser T, Stathopoulou T, Christodoulidis S, Anthimopoulos M, Mougiakakou SG, Ebner L (2019) Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images. Invest Radiol 54(10):627–632 18. Shukla SK, Singh DP, Gupta S, Joshi K, Gupta VK (2022) A theoretical graph based framework for parameter tuning of multi-core systems. Int J Wirel Microw Technol 12(4):15–25 19. Kapil D, Mishra SK, Gupta VK (2022) A Performance perspective of live migration of virtual machine in cloud data center with future directions. Int J Wirel Microw Technol 12(4):48–56

Lung Conditions Prognosis Using CNN Model Harshit Jain, Indrajeet Kumar, Isha N. Porwal, Khushi Jain, Komal Kunwar, Lalan Kumar, and Noor Mohd

Abstract The paper is about the optimal lung disease prediction model. Basically, in this, multiple lung diseases are detected by training a convolutional neural network. And used a deep learning algorithm as it works well with large number of datasets as various dimensions and features of images as well as textual data can be investigated. Lung infections/diseases can be detected by taking blood tests etc. but it is expensive so the main objective of creating this model is to predict using X-Rays to reduce the cost and time of the conduct. Image classification models and segmentation techniques like VGG-16, ResNet50, etc., can be utilized to detect lung infections and illnesses. The semantic gap between the high-level semantic information that humans perceive and the low-level visual information that imaging technologies collect is the fundamental disadvantage of old approaches. The deep convolutional neural network was developed because of the difficulty in maintaining and querying enormous datasets. The developed model gives an accuracy of 82.6% with the loss of 0.2396. The obtained results show the model performance is good and can be used as a secondary opinion tool by medical experts. Keywords Deep learning models · Lung diseases · Convolutional neural network · Classification algorithm

H. Jain · I. Kumar (B) · I. N. Porwal · K. Jain · K. Kunwar Graphic Era Hill University, Dehradun, UK, India e-mail: [email protected] L. Kumar G. L. Bajaj Institute of Technology and Management, Greater Noida, UP, India N. Mohd Graphic Era University, Dehradun, UK, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_20

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1 Introduction Different sorts of disorders that impair the lungs’ normal function are referred to as lung diseases. They have an impact on pulmonary and respiratory processes, including breathing and lung health [1–3]. There are several lung conditions that are brought on by bacteria, viruses, or other fungal infections, environmental changes, and other variables such as asthma, carcinoma, and mesothelioma, also contribute to lung disorders [4–7]. The subsequent disorders for which our study, Lung Condition Prognosis has provided are Infiltration, Pneumonia, Hernia, Atelectasis, Cardiomegaly, Effusion, Mass, Nodule, Consolidation, and Pneumothorax. The first description of artificial intelligence (AI) was made in 1950. However, it wasn’t until the 1970s when expert systems like INTERNIST-I, MYCIN, ONCOSIN, etc., began to use AI in medicine. Before 1980, the US’s use of artificial intelligence in medicine was largely constrained. On September 13th and 14th, 1985, an international meeting was held in Italy. The “Society for Artificial Intelligence in Medicine” was founded in 1986 to foster an active research community. The organization hosts international conferences every two years. One problem with using artificial intelligence in medicine is that there isn’t enough data. This problem can be remedied by using electronic medical records (EMR). EMR was first proposed by Larry Weed in the late 1960s. In the 1970s, the US government implemented EMR through the Department of Veteran Affairs. Worldwide, one of the leading causes of death is respiratory illness. The World Health Organization estimated that four diseases caused about one-tenth of all disability-adjusted life years (DALYs) lost globally in 2008. One-sixth of all fatalities in 2008 were attributable to chronic obstructive pulmonary disease (COPD), cancer, and lung infections (mostly pneumonia and TB). Cancer (ranked 24th in 1990), COPD (ranked sixth in 1990 and ninth in 2010), and TB were all among the top 25 reasons (ranked eighth in 1990 and thirteenth in 2010). The medical industry makes substantial use of machine learning techniques. Finding hidden patterns in enormous amounts of data that is utilized for clinical diagnostics has a lot of potential with data mining. Health businesses may use data mining to analyze data systematically, find inefficiencies, and pinpoint best practices that enhance patient care while reducing costs. Identification of lung disorders is one of the largest challenges, and several researchers are working to assist physicians by creating sophisticated algorithms for making medical judgments. An investigation using machine learning methods is performed to anticipate the likelihood of developing lung illness before it did. The sole goal of this study was to determine the accuracy levels of bagging, logistic regression, and random forest. Deep learning strategies categories were learned gradually owing to their hidden layer design by initially producing low-level categories like letters, then high-level categories like words, and finally high-level categories like sentences. The network’s neurons and nodes generated a complete representation of the picture, with each representing a different aspect of the whole. As the model improved, the weights of each node or hidden layer were adjusted to reflect how tightly related it was to the

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output. Conventional machine learning approaches required a domain expert to identify the bulk of the important attributes. This is because traditional machine learning techniques could not simplify the data and make patterns more obvious, which was essential for learning algorithms to work. The main benefit of deep learning algorithms was that they tried to gradually learn high-level qualities from data. As a result, hard-core feature extraction and domain expertise were no longer necessary [8]. A brief description of previously published literature studies is given in Table 1. In the above literature review, different journals and papers are mentioned related to lung disease prediction and have used different machine learning and deep learning models with different accuracy for their models. Some of the models that are used to predict diseases are Mobile Net, Dense Net, ResNet, VGG-16, and some machine learning models like logistic regression, Random Forest, etc., have been used. And here it is clearly seen that majorly deep learning models like CNN model are used to Table 1 A brief description of previously published literature studies Reference No.

Methodology

Objective

Meraj et al. [9]

Convolutional neural network (CNN) VGG-16 model

Detection of pulmonary 77.14 tuberculosis manifestation in chest X-Ray

Accuracy (%)

Rehman et al. [10] Deep learning, segmentation (U-Net models), and visualization

Reliable tuberculosis detection using chest X-Ray

96.40

Ardila et al. [11]

Custom deep learning model

End to end lung cancer screening with 3D deep learning on low-dose chest computed tomography

94.40

Ausawalaithong et al. [12]

Deep learning approach, CNN, dense net

Automated lung cancer 74.43 prediction from chest X-Ray

Tahir et al. [13]

Segmentation U-Net, classification pre-trained CNN model

Deep learning for reliable classification of covid-19, MERS, SARS from chest X-Ray images

96.94

Sen et al. [14]

K-Fold cross-validation technique, Random Forest, logistic regression, logistic model tree, bagging Bayesian networks

In depth analysis of lung disease prediction using machine learning algorithm

91.50

Gite et al. [15]

Deep learning, U-Net++ model Enhanced lung image segmentation using deep learning

98.00

Souid et al. [16]

Deep learning, MobileNet Classification and model, multilabel classification predictions of lung diseases from chest X-Rays using MobileNet V2

90.00

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detect and predict lung diseases/infections. The deep learning model and techniques used in the research paper Deep learning for reliable classification of covid-19, MERS, SARS from chest X-Ray images is Segmentation U-Net, classification using CNN models which are pre-trained and has a higher accuracy of 96.94%. Kumar et al. [17] used CNN for the tissue pattern classification using mammographic images and it shows outstanding performance. Therefore, CNN is used for the lung condition prognosis.

2 Research Objective The main objective was to create a prediction engine that will enable consumers to determine if they have lung illness while sitting at home. If the user does not have lung disease, he does not need to see a doctor for additional therapy. To forecast the presence of the disease, the prediction engine needed a sizable dataset and effective machine-learning techniques. Before pre-processing, to train the dataset the deep machine learning models, redundant, null, or invalid data was removed to enhance the performance of the prediction engine. To treat patients, physicians used common sense. When broad information was lacking, research was summarized after a given number of cases were examined. While machine learning facilitates early pattern identification, this process took time. To employ machine learning, there needed to be a vast amount of data. The data was often scarce and depended on the illness. In addition, the number of samples free of illnesses outnumbered the number of samples with diseases. Two case studies were undertaken in this study to evaluate the efficacy of various machine learning techniques.

3 Methodology The first step was to create a custom X-Ray dataset for the 14 lung diseases. And we collected them from different labs and hospitals. After that, we used different object extraction models to extract the lungs from the X-Ray images, i.e., the required region, and remove the unwanted regions. The primary or major goal of doing this was to decrease the expense, and time, and to improve the model’s ability to forecast lung ailments. The task was to detect the diseases from abstracted lung images and for which some deep learning algorithms were used. The most basic DL algorithm is perceptron, and it is inspired by neurons (present in the human body). But we used CNN and not perceptron of ANN because the input image passed to the model was flattened directly i.e., the input image in a form of a matrix was converted from n × n to 1 × n × n and was flattened which led to overfitting of data, cause of dimensionality and variance in object position. To overcome these problems the CNN algorithm was

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used in which the feature extraction was done before flattening the image. In this, the features were extracted, and the dimensions were reduced. Template matching was done which is a technique used in CNN, a digital image processing to find a small part of the image a filter was generated here which was moving and generally of a 3 × 3 matrix, and that filter did match with every pixel unlike in ANN which has a static one. Figure 1 shows the methodology flow chart. Firstly, the input data is taken and after that preprocessing is performed on the data inputted after preprocessing, which is the removal of missing values, noises in the data, etc., is done, the data bifurcation is performed that is dividing the dataset into test and train data and this has been done because we cannot train the model on single dataset and if we did so then it will not be able to assess the performance of the model. Therefore, there is a need to separate the data into train test and validation datasets. Fig. 1 Methodology

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After that the model is trained on the train dataset and the resulting model M1 will be used to validate the test dataset and the final decision will be made, i.e., finding or no finding.

4 Algorithm Working First, the input image was given to the model, then the required and the wanted area i.e., the image was in the form of a matrix and the lung was extracted from the image. A filter was applied called the filter template which was matched with every pixel. Then multiplication was done, and convolution layer was generated here and then pooling was applied to it. The neural network with its many hidden layers, convolution layers, pooling layers, fully connected layers, and activation functions, etc., is analogous to deep learning model in this regard. Classification and object detection issues were good candidates for some well-known neural network architectures like CNN and recurrent neural networks (RNN) which are pre-trained [2]. For non-medical professionals, it was challenging to recognize and extract DICOM information from a picture without first understanding what information it contained. All medical images were in the DICOM format, which had the extension “.dcm.” Therefore, 35 important data elements could be retrieved from a DICOM picture by utilizing the following scripts. “Medicom” was utilized to solve this purpose, which is a python package for inspecting and modifying DICOM files. The working of model is given in Fig. 2. Average = {(17 + 16 + 16 + 15)/4} = 16 The convolution layers were used by CNN as a feature extractor layer on the input image. The input image was convolutional convoluted with the convolutional layer’s weight matrix to calculate the feature. Each neuron’s output was the result of multiplying a subset of the input image by dots and the convolutional layer’s weight matrix. The output image’s size was specified as Output size (W) = {(W − F + 2P)/(S + 1)}

(1)

Output size (H) = {(H − F) + 2P}/(S + 1)

(2)

where W and H represent the image’s width and height. P is the pooling function, S is the stride value, and F is the kernel filter [3]. The parameters that were calculated included the final determination of whether the disease was present in the lungs or not, i.e., no findings or healthy lung, as well as the model’s accuracy, loss, sensitivity, specificity, and plot various maps, such as heat maps, among other things.

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Fig. 2 Algorithm working

5 Result Analysis 5.1 Dataset The dataset used here for the implementation of this research work consists of data images of lungs collected from different hospitals. In this research work, 10 different diseases have been tested. The data images have been divided into two parts as one part is used for training and another part is used for testing process. The training data set consists of 90 images of every disease we are testing, and the testing dataset consists of 10 images of each disease which in total makes 100 images of each disease utilized in carrying out the research work.

5.2 Experiment Analysis The experiment carried out for this work is based on the self-developed CNN model. The developed CNN model consists of an input layer of size 600 × 600 × 3, dropout layer, average pooling layer, and dense layer. For every layer “relu” activation function is used. After passing all images to the model, a total of 64,952,958 parameters are extracted. Among these parameters, 64,801,534 are used for training purposes and the remaining 151,424 are treated as non-trainable features. The summary of the model is given in Fig. 3.

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Fig. 3 Model summary

The entire experiment has been executed at “Jupyter notebook” interface. After the execution of the model is evaluated for 35 epochs with early-stopping features. Therefore, the experiment stops at epoch number 12. The obtained results are measured in terms of Training loss, training accuracy, validation loss, and Validation accuracy. The obtained graph is shown in Figs. 4 and 5, respectively.

Fig. 4 Training and validation loss curve of used model

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Fig. 5 Training and validation accuracy curve

The obtained accuracy of the model after 13th epoch is like 90.6% of training accuracy, 0.1838 is training loss, 82.6% of validation accuracy and 0.2396 is validation error. The error and validation accuracy can be further enhanced by using various parameters tunning. This can be performed by the authors in their next work.

6 Conclusion This work presents the working of different CNN for the automated detection of eleven different lung diseases using chest X-Ray images. The self-designed CNN model has been used for the study and performance of the model is computed in terms of the training accuracy, testing accuracy, training loss, and validation loss. This study aimed to achieve accurate and error-free prediction of diseases while using minimal manpower and small model architectures. To increase the accuracy of the work, the segmentation of the lung’s X-Ray images was carried out which was a crucial step in order to reach precision using radiographs. It eliminated the noisy data which was not required for the prediction of diseases. The study done has proved to be helpful in the fast and accurate detection of lung diseases and has the potential to save many lives that are lost due to incorrect and delayed diagnoses.

References 1. Frade J, Pereira T, Morgado J et al (2022) Multiple instances learning for lung pathophysiological findings detection using CT scans. Med Biol Eng Comput 60:1569–1584. https://doi. org/10.1007/s11517-022-02526-y

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2. AliboniAlibi L, Pennati F, Gelmini A, Colombo A, Cioni A, Milanese G, Sverzellati N, Magnani S, Vespro V, Blasi F, Aliverti A (2022) Detection and classification of bronchiectasis through convolutional neural networks. J Thorac Imaging 37(2):100–108 3. Chauhan R, Ghanshala KK, Joshi RC (2018) Convolutional neural network (CNN) for image detection and recognition. In: 2018 first international conference on secure cyber computing and communication (ICSCCC). IEEE, pp 278–282 4. Tiwari P, Pant B, Elarabawy MM, Abd-Elnaby M, Mohd N, Dhiman G, Sharma S (2022) Cnn based multiclass brain tumor detection using medical imaging. Comput Intell Neurosci 5. Bian Y, Zheng Z, Fang X, Jiang H, Zhu M, Yu J, Zhao H, Zhang L, Yao J, Lu L, Lu J (2022) Artificial intelligence to predict lymph node metastasis at CT in pancreatic ductal adenocarcinoma. Radiology 6:220329 6. Chen X, Sun S, Bai N, Han K, Liu Q, Yao S, Tang H, Zhang C, Lu Z, Huang Q, Zhao G (2021) A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy. Radiother Oncol 1(160):175–184 7. Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers R (2017) ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: IEEE CVPR, pp 3462–3471 8. Tiwari P, Upadhyay D, Pant B, Mohd N (2022) Multiclass classification of disease using CNN and SVM of medical imaging. In: International conference on advances in computing and data sciences. Springer, Cham, pp 88–99 9. Meraj SS, Yaakob R, Azman A, Rum SN, Shahrel A, Nazri A, Zakaria NF (2019) Detection of pulmonary tuberculosis manifestation in chest X-rays using different convolutional neural network (CNN) models. Int J Eng Adv Technol (IJEAT) 9(1):2270–2275 10. Rahman T, Khandakar A, Kadir MA, Islam KR, Islam KF, Mazhar R et al (2020) Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization. IEEE Access 8:191586–191601 11. Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L et al (2019) End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 25(6):954–961 12. Ausawalaithong W, Thirach A, Marukatat S, Wilaiprasitporn T (2018) Automatic lung cancer prediction from chest X-ray images using the deep learning approach. In: 2018 11th biomedical engineering international conference (BMEiCON). IEEE, pp 1–5 13. Tahir AM, Qiblawey Y, Khandakar A, Rahman T, Khurshid U, Musharavati F et al (2022) Deep learning for reliable classification of COVID-19, MERS, and SARS from chest X-ray images. Cognit Comput 1–21 14. Sen I, Hossain M, Shakib M, Hossan F, Imran M, Faisal, FA (2020) In depth analysis of lung disease prediction using machine learning algorithms. In: International conference on machine learning, image processing, network security and data sciences. Springer, Singapore, pp 204–213 15. Gite S, Mishra A, Kotecha K (2022) Enhanced lung image segmentation using deep learning. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06719-8 16. Souid A, Sakli N, Sakli H (2021) Classification and predictions of lung diseases from chest X-rays using MobileNet V2. Appl Sci 11(6):2751. https://doi.org/10.3390/app11062751 17. Kumar I, Kumar A, Kumar VD, Kannan R, Vimal V, Singh KU, Mahmud M (2022) Dense tissue pattern characterization using deep neural network. Cognit Comput 1–24

Stock Trend Prediction Using Candlestick Pattern Divyanshu Bathla, Ashish Garg, and Sarika

Abstract The stock market is the place where buyers and sellers come to buy and sell their stocks to get maximum ROI. Stocks go up and down because of the law of supply and demand. The stock market is nonlinear in nature and prediction of stock market trends is a tedious task because of its property to easily get affected by a lot of parameters such as stock and company-specific news, company profile, public sentiment, global economy, etc. Over many years prediction of the stock market trend is a temping problem for most data scientists. To study the massive data generated by the stock market and to perform fundamentals and technical analysis ML and deep learning techniques are effectively used. The fundamentals analysis is generally based on Earnings before interest, taxes, and amortization (EBITA) sheet, quarterly results of stocks whereas technical analysis can be done on daily basis by observing moving averages, candlestick patterns, etc. With the research, it has been observed that the stock market forthcoming trend is highly correlated with candlestick patterns generated in stock markets. There are 42 different candlestick patterns that can form in a timeframe varying from 1 to 4 days but in this paper, only those candlestick patterns have been included which have a timeframe of 1 day. In this paper, a technical analysis-based model has been proposed which uses 4 different candlestick patterns having a timeframe of 1 day to predict the forthcoming trend and the proposed model got an accuracy of 66%. Keywords Candlestick patterns · ML · Deep learning · ROI

D. Bathla (B) · Sarika Computer Science Graphic Era Hill University, Dehradun, India e-mail: [email protected] A. Garg Computer Science Graphic Era Deemed to be University, Dehradun, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_21

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1 Introduction The stock market is the place where stocks of companies registered on the NSE are bought and sold. The stock market is a component of a free-market economy. It allows companies to raise money by offering some equity of the company in the form of stocks and allows investors to participate in the financial gains and making a profit through it. Investors can trade on the stock market with the help of stockbrokers, portfolio managers, and investment bankers. Stock prediction refers to the prediction of future market stock trends which can lead to an increase in profit. In the past few years, a lot of people have started investing in the stock market to earn a profit. The stock market can be extremely fluctuating even at the scale of milliseconds which can lead to competition in the high-density trading field [1] or the long period economic boom and bust cycles [2] which can have an impact for several years. The dynamic or non-linear nature of the stock market makes it difficult predict for investors to invest in the stock market, and accurately predicting forthcoming trends is an extremely difficult task as it gets affected by a lot of factors such as public sentiment, company growth, etc., But advancement in technology and machine learning makes it possible to predict the dynamic structure of the stock market. Nowadays machine learning and its sub-domains are efficiently used to predict the stock’s forthcoming trend and the result generated by the machine learning is used by the investors to invest accordingly. The entire idea of predicting stock prices is to gain significant profit. In the past many years, mainly two analyses have been performed on the data to predict the forthcoming trend which is technical analysis and fundamental analysis [1]. The first approach is technical analysis in which indicators like convergence/divergence, average, Meta sine wave, candlestick patterns, etc. have been used. Whereas the second approach is fundamental analysis which is mainly based on the company profile, market status, current world economy, EDITA sheet, etc. [3]. Technical analysis is mainly used for short-term trading and fundamental analysis is preferred for mainly long-term investment [4]. Major contribution of this article are as follows: 1. Out of 7 candlestick patterns having a timeframe of 1 day finding out the 4 candlestick patterns which provide better accuracy than others. 2. Algorithm of candlesticks pattern used to create model. 3. Proposed a technical analysis-based model which used candlestick patterns to predict the forthcoming trend of stock market.

2 Literature Review The prediction of the stock market is always one of the most tempting topics in the field of data science because of its non-linear and dynamic in nature which makes it hard to predict. In the past many decades, many researchers come up with different methodologies and models to predict the market.

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Sadia et al. used SVM and RF algorithms for Stock market prediction where a multi-gamma model along with a random forest model has been used and experimental results show that the algorithm gives an accuracy of 81.6 percent [5]. Hung et al. predicted stock value using stock-specific new articles and historical data of the stock market where the Naïve Bayesian classification method and KNN and got an accuracy of 65.3–91.2% respectively and the experimental result shows that KNN outperform naïve bayes [6]. Vijayakumar et al. used Natural Language Processing techniques and got an accuracy of 85% Experimental result shows that NLP can be used for stock market trend to get good accuracy [7]. Chandar proposed a DWT-ANN model, and 92.5% accuracy is obtained. Experimental result shows that the proposed model gives comparable accuracy to the existing models [8]. Andrew et al. perform an experiment and shows that the result of technical analysis can be improvised by combing them with automated algorithms and using artificial intelligence-based techniques to get an accuracy of 65% [9]. Ho et al. use candlestick Chart Representation and Sentiment Analysis techniques and the proposed method obtained a considerable result of 75.38% for Apple stock [10]. Hong et al proposed a rebalanced and clustered SVM model for finding depend on variance and the proposed methodology gives an accuracy of 80% [11]. Behera et al. predicted the customer review’s opinion with the help of Short-Term Model LSTM based model and got an accuracy of 93.66% [12]. The experiment result shows that sentiment analysis gives considerable accuracy. T. Fischer et al. proposed an LSTM network model and observed that the proposed model gives better results than the memory-free methods and gives significant results of 0.46% per day statistically and economically respectively which is better than the result of RAF and logistic regression which gives results of 0.43, 0.26, respectively [13]. Bijesh et.al proposed a technique that uses the ARIMA model and the experimental results of the proposed technique show that the price predicted by the proposed technique was close to actual price of different stocks [14]. Lauguico et al. proposed a fuzzy model and the result of the proposed model can be used to give stock worth. The value of the stock market is calculated by the Dividend discount model and the proposed model gives an accuracy of the model is 0.77% [15]. Madbouly et al. proposed a model which provides a solution to various challenges like noise, uncertainty nonlinearity, and uncertainty that is present in stock market trends. The experimental results show that the proposed model is feasible to implement and gives high forecasting accuracy [16]. Long et al. proposed a multi-filter neural network (MFNN) model for the task of sample price movement and feature extraction prediction of financial time series and the proposed model using a neural network for signal-based trading simulation and the extreme market prediction of the CSI 300 index [17]. Neelima et al. performs a study on ANN and attempted to disclose the reason ANN is being used as the universal technique for time series prediction with great accuracy [18]. Andriyanto et al. proposed a CNN-based model and Prediction and gave Candlestick Patterns as input Images. And got an accuracy of up to 99.3% accuracy [19]. Kusuma et al. predicted the market trend using Candlestick Chart and Deep Learning Neural Networks. The experimental results show that the proposed model provides considerable accuracy of 92.2 and 92.1% when an experiment is

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performed on Taiwan and Indonesia stock market datasets [20]. Liang et al. proposed a sequence similarity and candlestick patterns-based model which gives accuracy of 56.04 and 55.56%, which is higher than the LSTM model (50.71 and 50.68%) and SVM model (50.83 and 51.32%). Experimental result shows that the proposed model outperforms LSTM and SVM model [21].

3 Methodology It is observed that there are many candlesticks pattern which whenever form in the stock market change the market trend to upside down. But manually checking the candlesticks pattern over a lot of stocks is a tedious task so in this paper authors are attempting to automate the task with the help of machine learning. To automate the task of identification the candlestick pattern includes a number of steps as shown is Fig. 1.

3.1 Data Collection To collect the historical data of different stocks authors are using yahoo finance, but yahoo finance needs the ticker of each stock which is downloaded by National stock agency along with the company name. Fig. 1 Workflow of the proposed model

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3.2 Date Pre-processing The data is divided into two parts in which first part contains the historical data of previous 4 days to find the on-going trend and another part contain the data of the date before the date of prediction to detect the candlestick pattern.

3.3 Candlestick Identification Algorithm There are more than 10 candlesticks patterns exist which whenever form change the stock market trend upside down but, in this paper, authors are only using four candlesticks, i.e., Marubozu, Spin top, Hammer, Inverted hammer, and the proposed algorithm to automatically detect the stock market.

3.4 Signal Generation If the candle is formed, then the model can give a buy and sell signal to the investors based on the ongoing trend.

4 Proposed Model The proposed model is based on the technical analysis of data where 4 different candlesticks having timeframe of 1 day are used. Although in the timeframe of 1 day there exists 7 different candlesticks which are Hammer, Spin top, Marubozu, hanging man, shooting star, Inverted hammer, Doji but out of them Hammer, Spin top, Marubozu, Inverted hammer are providing better accuracy as compared to others and because of their better accuracy they are used in the proposed model. There are two types of candlesticks patterns: Bullish candlestick pattern These patterns form when the market is in downtrend, and it indicates that the market trend may change to an uptrend, and this can be the right time to buy stock as to gain more profit. Bearish candlestick pattern These patterns form when the market is in uptrend, and it indicates that the market trend may change to downtrend, and this is the time when the stocks can be sold (Fig. 2).

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Fig. 2 Workflow of the proposed model

The proposed model is used to predict 4 different candlestick pattern which can be used as both bearish candlestick pattern and bearish candlestick pattern as if these candle form in bearish trend change there is a high chance that forthcoming trend will be bullish and vice-versa.

4.1 Candlestick Patterns Marubozu candlestick This pattern form when the absolute difference between open of day and close of day is at least 5–7 time greater than the absolute difference of high of day and close of day and absolute difference of open of day and low of day.

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ALGORITHM Marubozu 1. 2.

def Marubozu(open_close,low,high) if((open_close>high*5 and open_close>low*5) or (open_close>high*5 and open of day>high of day) or(open_close>low*5 and close of day5*open_close and high>5*open_close and abs(high-low) 5*open_close and high>5*open_close and abs(high-low) Raspberry Pi Configuration. Click on Interfaces and set SSH to Enabled. Click OK. Or else command line can be used to enable SSH terminal within the command “Sudo raspi-config” which will enable SSH on the pie module. Activate SSH Client in Windows. Linux and macOS both support SSH, Windows 10 supports SSH, but needs to be activated. Go to Manage Optional Features -> Settings window -> Add a feature -> Open SSH Client (Beta) -> install. Get IP address. Raspberry Pi can be connected to a local network. Use wireless LAN or connect Raspberry Pi directly to a router with an Ethernet cable. Using command line “Hostname-I” will return the Ip address of raspberry pi.

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Connect via SSH. “ssh pi@[IP]” will take the Ip of raspberry pi which was obtained earlier. Typing the following command on our command line will connect through raspberry pi. It will ask for authentication and raspberry pi username and password. Next step is to connect and program the GSM module. To send an alert message, setup a connection of GSM modem having built-in GSM module with our Raspberry Pie. Setting up connection. So, raspberry pie has 40 pin GPIO connection pins as seen in Fig. 7. to communicate with external hardware. The pin Configuration of Raspberry pie is as follows— Now, a GSM modem is needed to perform SMS alert-sending process. For this requirement SIM800c in Fig. 8. modem is needed which will have a sim card port in which sim card is inserted and messages are set. Next is to connect the SIM800c modem with our Raspberry pie. Fig. 7 SIM800c GSM modem

Fig. 8 Raspberry pie to GSM modem connection

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Fig. 9 Configuration menu

To connect it with Raspberry pie, connect the TXD(GPIO14) and RXD(GPIO15) of Raspberry Pie with the TXD and RXD of SIM800c. And along with these, connect the VCC 5 V and GND with the Raspberry Pie as shown in Fig. 8. Now, after setting up the hardware connection, open the terminal of Raspberry pie and configure it to communicate with SIM800c modem. Configuration and Programming of Raspberry pie to Communicate with the GSM SIM800c modem. Either open the Raspberry pie OS to open terminal or connect the Raspberry pie with computer and open SSH terminal. Now it is needed to communicate with the terminal via following code— First, open configuration menu by using “sudo raspi-config”. A configuration menu will appear as seen in Fig. 9. Now, select option 5, i.e., Interfacing Options, after that, an interface as shown in Fig. 10. Select P6 enable/disable shell and kernel message. It will now ask “would you like a login shell to be accessed over serial?” Select “No”, (It will reduce repetitive login that will be asked every time you access serial port). Then it will ask “would you like the serial port hardware to be enabled?” Select “Yes”. (It will use pin 8 (GPIO 14) and pin 10 (GPIO 15) as serial port now). Then, click finish and reboot. After the reboot, disable Bluetooth and disable the system service that initializes the modem. To do so again open terminal and write “disablebt” and “sudo systemctl disable hciuart”. After this much, create a python file as shown in Fig. 11 that can do the processing and send SMS. Figure 11 contains the program which performs a basic functionality to check the presence of pulse. The signal received from pulse sensor is directed to ADC module which will convert the analog signal received from the sensor into digital and transmit Fig. 10 P6 enable/disable shell and kernel message

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Fig. 11 Python code used to program Raspberry Pi

it to the Raspberry Pi. Considering the fact that the Raspberry Pi doesn’t have analog input, ADC module also plays a crucial role. The program checks the presence of a pulse by detecting the high voltage from the pin receiving the signal. If there is no pulse in 10s, then the program will initiate the process of sending an alert message to the a mobile number. So, in the architecture described earlier, there is a pulse sensor connected with ADC module which is connected to the Raspberry pie to receive the data from sensors. So, let’s say pin 24 of Raspberry pie receives the input signal of pulsometer from ADC module. Then open any text editor on the Raspberry Pie OS and write the following code save this code as messalert.py on desktop. And it is good to go. Auto Run the python script at the boot Time. In BSN, it is must to make a system that do need to get configured by us every time it is booted. So, it is required to make the raspberry pie to be a standalone device that as soon it boots up, it autorun the python code and start performing its task. To do so, open the SSH terminal and do the following tasks. First, configuration menu is opened by using “sudo raspi-config”. A configuration menu will appear shown in Fig. 12. Now, select 3 boot option. A screen will open As shown in Fig. 13. Select desktop/CLI, A screen will open as shown in Fig. 14. Select B2 console autologin, A screen will open as Shown in Fig. 15. Select tab couple of times and then finish and reboot.

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Fig. 12 Configuration menu

Fig. 13 Configuration menu

Fig. 14 Configuration menu

Now open the root directory. Open SSH terminal Again and type “cd/” only to go to root directory. Now edit the boot file. In the root directory type “sudo nano / etc./profile”. And at the very end of the file type. “sudo python /home/pi/desktop/ messalert.py”. then finally reboot. Now our raspberry pie is fully setup to perform the task. Just complete all the connection, attach all the sensor and then power the

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Fig. 15 Configuration menu

raspberry pie, as soon as you power it up, it will boot up and run our python script and will start performing its functionality.

5 Challenges of BSN WBSN is an innovation that has its aim to make human life easier and reduce loss. With this, it must be risk free as well. But just like any other innovation, creating modern technology introduce new challenges and risks. The challenges that are faced in BSN are as follows—

5.1 Reliability The information needs to be reliable and precise. This reliability relies on many aspects such as communication between nodes, precision of sensors and the time latency. Lowering these factors are a big challenge in BSN [1]. To produce information that is reliable enough is a very challenging task and an important one as well. The information received from the sensors is used to predict the health condition of the patient. False prediction can affect so many factors such as time, cost, money, etc.

5.2 Privacy and Security There are several issues regarding the privacy and security of the data that has been uploading on the database [14]. It must be secure to protect it from unauthorized

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access [15]. There are several solutions to secure the data but still advancing technology open more vulnerability [1]. There are several technologies exist in today’s world for live uploading of data into the database with a design that includes authentication properties such as ‘end to end link.’ Typical performance metrics considered are computation cost, communication overhead, sender and receiver memory buffer size, authentication delay, and loss tolerance [7].

5.3 Portability Miniaturizing the size of sensors or sensory node is also a big challenge. It must be small and lightweight [1]. As BSN sensors are planted on the body as well as can be planted inside the body, these sensors have to be small in size.

5.4 Energy Consumption Energy consumption and power management are also a big challenge as it is important to make a BSN product that can last as long as possible making it more usable and dependable both [4, 15]. Many techniques are used to solve the problems related to energy consumptions. Those techniques include energy harvesting. Renewable energy is harvested from solar, vibrations or thermal energy [16]. Idea is to convert the human body heat into usable energy. Wearable devices can be powered by small fraction of energy generated from human body with the help of thermoelectric devices.

5.5 Sensitivity BSN users wear this sensor in different harsh environment which will affect the transducers of the sensor devices. It is a challenge to make the sensors durable [9, 13].

5.6 Effect of Electronics on Human Body Implanted sensors are electronic devices which contain diode, transistors, etc. These small electronic units produce heat when used. This heat from implanted electronics can damage the human body tissues and skin [16].

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6 Recent Research, Current Applications, and Use of Bsn in Different Industries Several current commercial applications of wireless physiological measurement systems [1] are mentioned in Table 1. The table is representing that there are industries present that are researching and developing Commercial Applications of BSN. The advanced implementation of BSN using multiple sensors and networking is displayed in the product. These commercial applications can be utilized in health care industries and can generalize the use of BSN. In the recent research paper [17], BSN has advanced to a level where health monitoring is accompanied with AI techniques to further evaluate the live data and recognize the changes in health pattern predicting the possibility of disease. The BSN accompanied with AI can help in various ways for predicting the possibility of disease with much more ease and accuracy. This act as a good support utility provided to the doctors. Not only this but also in paper [18], BSN has been utilized to measure the health conditions of military Soldiers to provide live monitoring of health and based on that, proper actuators are planted to treat the soldiers on spot providing immediate treatment. Along with this while training the soldiers, health-related data is being recorded, and based on the data proper training plans are created to prepare them more properly for future engagements. Recent research areas such as these show the potential of BSN in different industries. The engagement of BSN and idea to utilize it in different industries can be elaborated through Fig. 16.

Table 1 Current commercial applications of wireless physiological measurement systems Commercial applications

Vendor

Description

Market

TeleMuse

Biocontrol systems

This is a mobile physiological monitor for acquiring ECG, EMG, EOG, EEG, and GSR data from wireless sensors using ZigBee technology

Medical care and research

VitalSense integrated physiological monitoring system

VitalSense

This is a chest-worn wireless physiological monitor that incorporates an ECG-signal processor and offers wireless transmission of Heart Rate and Respiration Rate to a handheld monitor

Fitness and exercise

The alive heart and activity monitor

Alive

This Bluetooth device monitors heart rate and activity, including ECGs, blood oximeters and blood glucose meters. It communicates with software on your mobile phone to log and upload information to a central internet server

Medical care, research, fitness and exercise

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Fig. 16 Applications of BSN in different fields

7 Conclusion This paper concludes the need of BSN and its future potential. Along with this, what are the basic technologies that are used in BSN are also well described. These technologies must work together in a well-organized architecture which is also raised in this paper. The proposed approach displays the basic implementation of BSN and if compared with the old system, the proposed system provides mobility to the user and a quick alarming in critical condition. BSN is already in implementation in some places but still majority do not use/have BSN. The paper has given proper awareness to increase the utilization of this technology as aimed. Finally, the challenges that were faced by various researchers are also mentioned to expose the node of improvisation, and current real-world Implementations of BSN are shown.

References 1. Hao Y, Foster R (2008) Wireless body sensor networks for health-monitoring applications 2. Aziz O, Lo B, King R, Darz A, Yang G-Z (2006) Pervasive body sensor network: an approach to monitoring the post-operative surgical patient 3. Lo BPL, Thiemjarus S, King R, Yang G-Z (2005) Body sensor network—a wireless sensor platform for pervasive healthcare monitoring 4. Pentland A (2004) Healthwear: medical technology becomes wearable 5. Habiba C, Makhoula A, Darazib R, Salima C (2017) Self-adaptive data collection and fusion for health monitoring based on body sensor networks

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6. Rajawat AS, Barhanpurkar K, Shaw RN, Ghosh A (2021) Risk detection in wireless body sensor networks for health monitoring using hybrid deep learning 7. Alia ST, Sivaramana V, Ostryb D (2013) Authentication of lossy data in body-sensor networks for cloud-based healthcare monitoring 8. Gope P, Hwang T (2016) BSN-care: a secure IoT-based modern healthcare system using body sensor network 9. Kulkarni SS, Mahajan SK, Mahato VR, Pawar SG, Pattanaik S (2017) A secure health care technology based on BSN care 10. Darwish A, Hassanien AE (2011) Wearable and implantable wireless sensor network solutions for healthcare monitoring 11. Sharma Gaur M, Gaur NK, Kumar S, Sharma PS (2022) Security risk analysis and design reengineering for smart healthcare. In: Sharma S, Peng SL, Agrawal J, Shukla RK, Le DN (eds) Data, engineering and applications. lecture notes in electrical engineering, vol 907. Springer, Singapore. https://doi.org/10.1007/978-981-19-4687-5_46 12. Braem B, Latre B, Moerman I, Blondia C, Demeester P (2006) The wireless autonomous spanning tree protocol for multihop wireless body area networks 13. Lai X, Liu Q, Wei X, Wang W, Zhou G, Han G (2013) A survey of body sensor networks 14. Kumar P, Lee H-J (2011) Security issues in healthcare applications using wireless medical sensor networks: a survey 15. Asam M, Butt SA, Jamal T (2019) Challenges in wireless body area network 16. Ayotollahitafti V, Ngadi A, Sharif JBM (2015) Requirements and challenges in body sensor networks: a survay 17. Manickam P, Mariappan SA, Murugesan SM, Hansda S, Kaushik A, Shinde R, Thipperudraswamy SP (2022) Artificial Intelligence (AI) and Internet of Medical Things (IoMT) assisted biomedical systems for intelligent healthcare 18. Pragadeswaran S, Madhumitha S, Gopinath S (2021) Certain investigations on military applications of wireless sensor networks

Brand Sentiment Analytics Using Flume Devanshi Sharma, Alka Chaudhary, and Anil Kumar

Abstract Applications that collect data in various forms can add data to the Hadoop stack by partnering with Name Node through an API capability. Using the entirety of the stockpiling and handling force of bunch servers and running dispersed processes on colossal volumes of information are simplified by Hadoop. The Hadoop building blocks can be utilized as an establishment for the making of a few administrations and applications. Keywords Name Node · API function · Server · Hadoop · Cluster · Services

1 Introduction The open-source Apache Hadoop structure can be utilized to store and deal with gigabytes to petabytes of information proficiently. In contrast with utilizing a solitary strong framework for information capacity and handling, Hadoop empowers grouping a few PCs to dissect enormous datasets in equal all the more rapidly. In 2005, Doug Cutting and Mike Cafarella created Hadoop. Cutting, which was used for Yahoo! until then, he named it after his child’s elephant-themed toy. It was at first made to empower dissemination for the task to construct a web index called Nutch. Hadoop Distributed File System (HDFS) stockpiling part and Map Reduce handling part make up its centre. Hadoop partitions records into significant blocks and scatters them among group hubs. In light of how much information must be handled, Hadoop disperses packaged code to hubs for equal handling. The dataset D. Sharma · A. Chaudhary (B) Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India e-mail: [email protected]; [email protected] D. Sharma e-mail: [email protected] A. Kumar School of Computing, DIT University, Dehradun, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_27

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can be handled more rapidly and successfully utilizing this strategy than it would utilize a more customary supercomputer engineering, which depends on an equal record framework and disperses calculation and information utilizing high velocity organizing [1]. Information region is the act of hubs controlling the information they approach. Google’s papers on Map Reduce and the Google File System were filled in as inspiration for the Map Reduce and HDFS portions of Hadoop. The Hadoop framework itself is written mainly in Java, with a light amount of C code and shell scripts for request line gadgets. Using the entirety of the stockpiling and handling force of bunch servers and running dispersed processes on colossal volumes of information are simplified by Hadoop. The Hadoop building blocks can be utilized as an establishment for the making of a few administrations and applications. Applications that assemble information in different configurations can add information to the Hadoop bunch by associating with the Name Node through an API capability. Each document’s “piece” position and record catalogue association are followed by the Name Node and duplicated across Data Nodes. The work of Give and Map Reduce contained various help and reduce efforts that are performed against data in HDFS distributed across data nodes to take place something essential to check data. To run something critical for data inspection, provide a Map Reduce job that contains many helper and mitigation tasks that conflict with data in HDFS spread across data nodes. Each centre point guides the auxiliary movement against the predefined input records and minimizes the competition for summation and coordination of the result (Table 1). The extensibility of the Hadoop ecosystem has allowed for substantial growth over time. Numerous tools and applications are now part of the Hadoop ecosystem and can be used to gather, store, process, analyse, and manage large amounts of data. The most well-liked programmes include: ● Spark—Huge information jobs as often as possible utilize the open source, circulated handling framework Spark. Apache Spark offers nonexclusive clump handling, streaming examination, AI, diagram data sets, and specially appointed look. It uses in-memory storing and upgraded execution for speedy execution. ● Presto—A circulated, open-source SQL question motor intended for fast, onthe-fly information examination. The ANSI SQL standard is upheld, and this incorporates progressed inquiries, collections, joins, and window capabilities. Table 1 Summary of abbreviations used

Abbreviation

Meaning

HDFS

Hadoop distribution file system

API

Application program interface

CSV

Comma separated values

TSV

Tab separated values

OLTP

Online transaction processing

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Various information sources, for example, the Hadoop Distributed File System (HDFS) and Amazon S3, can handled utilize Presto. ● Hive—Enables induction of Hadoop Map Reduce via SQL interface, enabling distributed and lean forgiving data warehouses despite huge extension rating [2].

2 MapReduce in Hadoop With the assistance of the Map Reduce system, we can make applications that dependably cycle enormous volumes of information in lined up on immense groups of ware equipment. A care process and model for a transport manipulation application is Java-based Map Reduce. The Map Reduce estimate is composed mostly of Map and Reduce. Using an aid, a large amount of data is transformed into another set where each component is divided into tuples (key/respect matches). The downstream job is a more manageable task that concatenates the data tuples from the helper’s output into a smaller, more discrete group of tuples. The reduction works are continuously completed following the auxiliary works, as the name Map Reduce derives. The essential advantage of Map Reduce is that it is so easy proportional information handling across various figuring hubs. The information handling basics utilized in the Map Reduce model are alluded to as mappers and minimizers. In some cases, it is hard to partition an information handling application into mappers and minimizers [3]. Regardless, scaling an After being created in the Map Reduce style, an application may operate on more than hundreds, thousands, or even a gigantic number of machines in a congregation with just a rearrangement. The Map Reduce model has been utilized by numerous software engineers on account of its clear adaptability. The Algorithm 1. The MapReduce model generally relies on getting the computer to the location of the data! 2. There are three stages to the MapReduce algorithm’s operation: the map phase, the shuffle phase, and the reduction phase. Map stage: Processing the input data is the responsibility of the map or mapper. As a rule, the information is put away in the Hadoop document framework as a record or registry (HDFS). The mapper capability gets the information record line by line. The information is handled by the mapper, who likewise creates various little information pieces. Reduce stage: This step is the after effect of consolidating the Reduce phase with the Shuffle phase. The processing of the information that is displayed from the mapper is the responsibility of the Reducer. After processing, it creates a new arrangement of the result to be stored in HDFS. During a Map Reduce job, Hadoop delivers the Map and Reduce jobs to the appropriate machines in the cluster.

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The framework manages all aspects of data-passing, including task assignment, verification of job completion, and data transmission across nodes in a cluster because most processing is done on nodes and data is kept locally on discs, there is less network traffic after completing the tasks at hand, the cluster gathers and reduces the data to produce the desired outcome before sending it back to the Hadoop server. Input Phase: This stage involves processing the input data or file. Hadoop divides the incoming data into smaller chunks known as "splits" in the second phase. Map Phase: Using the logic outlined in the map() function, MapReduce processes each split in this phase. At a given time, each mapper works on one split. Each mapper is viewed as a separate task, and each task is carried out using a different Task Tracker under the control of a Job Tracker. Combine Phase: This optional stage lowers the amount of data carried across the network, which enhances performance. The result of the map() function is aggregated by the combiner phase, which is identical to the reduce step, before being provided to the following steps. Shuffle and Sort Phase: Before moving on to the next stage, all of the mappers’ outputs are jumbled, sorted to place them in order, and grouped in this phase. Reduce Phase: Using the reduce() function, the outputs of the mappers are combined in this phase. The subsequent and last step receives the reducer’s output. Each reduction is viewed as a task, and each task is carried out across various Task Trackers under the supervision of the Job Tracker. The output of the reduction process is finally written to a file in HDFS [4].

3 SQOOP Sqoop is an innovation used to move information between social data set servers and Hadoop. It is utilized to trade information from Hadoop record framework to social data sets and import information from social data sets like MySQL and Oracle to Hadoop HDFS. One of the sources of Big Data is the conventional application management system, or the communication between applications and relational databases utilizing RDBMS. In the relational database structure, such Big Data produced by RDBMS is stored in Relational Database Servers. When the Hadoop ecosystem’s Big Data storages and analysers like Map Reduce, Hive, HBase, Cassandra, Pig, etc. emerged, they needed a tool to communicate with the relational database servers in order to import and export the Big Data that was stored there Here, Sqoop fills a role within the Hadoop ecosystem to enable practical communication between a relational database server and HDFS. Sqoop: “SQL to Hadoop and Hadoop to SQL”

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Sqoop is a technology made to transfer data between Hadoop and servers for social gatherings. It is used to move data from the Hadoop system of record to social information indexes and to import data from social information collections like MySQL and Oracle to Hadoop HDFS. The Apache Software Foundation offers it [5]. Sqoop Import Using the import tool, certain tables are imported from RDBMS to HDFS. Each table row is seen as a record in HDFS. In text files or Avro and Sequence files, all records are saved as binary data or as text data, respectively. Sqoop Export The export tool is used to export a set of files from HDFS back to an RDBMS. The files that are sent to Sqoop as input include records, which are also referred to as rows in a table. These are read, processed into a set of records, and then the user-specified delimiter is used to separate them.

3.1 Features And Limitations Of SQOOP 1. Strong: Apache Sqoop is exceptionally powerful in nature. It has local area backing and commitment and is effectively usable. 2. Full Load: Using Sqoop, we can stack an entire table just by a solitary Sqoop order. Sqoop likewise permits us to stack every one of the tables of the information base by utilizing a solitary Sqoop order. 3. Gradual Load: Sqoop upholds steady burden usefulness. Utilizing Sqoop, we can stack portions of the table at whatever point it is refreshed. 4. Equal import/trade: Apache Sqoop involves the YARN system for bringing in and sending out the information. This gives adaptation to non-critical failure on the highest point of parallelism. 5. Import aftereffects of SQL question: Sqoop likewise permits us to import the outcome got back from the SQL inquiry into Hadoop Distributed File System. 6. Pressure: We can pack our information either by utilizing the deflate(gzip) calculation with the—pack contention or by indicating the—pressure codec contention. We can stack a packed table in Apache Hive. 7. Connectors for all the major RDBMS Databases: Sqoop gives connectors to different RDBMS data sets, covering practically the entirety of the whole outline. 8. Kerberos Security Integration: Basically, Kerberos is the PC network confirmation convention that deals with the premise of the ‘tickets’ for permitting hubs that are imparting over the non-secure organization to demonstrate their character to one another. Apache Sqoop offers help for Kerberos verification. 9. Load information straightforwardly into HIVE/HBase: Using Sqoop, we can stack the information straightforwardly into the Hive for information examination. We can likewise dump our information in the HBase, that is to say, the NoSQL data set [6].

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4 Hive Enormous datasets that are put away in conveyed capacity and questioned utilizing SQL sentence structure can be perused, composed, and oversaw all the more effectively on account of the Apache Hive information stockroom programming. Hive, which is based on top of Apache Hadoop, has the accompanying abilities: ● Tools that make it simple for SQL to access data, facilitating data warehousing operations including extract, transform, and load (ETL), reporting, and data analysis. ● A technique for forcing association on various information designs ● Admittance to records kept in Apache HDFS or different information stockpiling frameworks like Apache HBase straightforwardly or by implication. ● Execution of queries using MapReduce, Apache Spark, or Apache Tez ● HPL-SQL procedural language Sub-second query retrieval with Apache YARN, Apache Slider, and Hive LLAP. Hive offers fundamental SQL capacity, including a considerable lot of the later elements for examination in SQL:2003, SQL:2011, and SQL:2016. Client characterized capabilities (UDFs), client characterized totals (UDAFs), and client characterized table capabilities can be in every way used to upgrade Hive’s SQL with client code (UDTFs). Information can be saved in an assortment of "Hive designs," not only one. Comma and tab-isolated values (CSV/TSV) text documents, Apache Parquet, Apache ORC, and more configurations all have inherent connectors for Hive. Hive can be extended by clients with connectors for additional configurations. For additional data, see to File Formats and Hive SerDe in the Developer Guide. Online transaction processing (OLTP) workloads are not what Hive is intended for. It works best for conventional data warehousing projects. Hive includes HCatalog and WebHCat among its components. Hive is intended to boost versatility (scale out with additional machines added progressively to the Hadoop bunch), execution, extensibility, adaptation to internal failure, and free coupling with its feedback designs [7–11]. Parts of Hive incorporate HCatalog and WebHCat. ● HCatalog is a table and board storage layer for Hadoop that enables clients with various data to take care of gadgets—To even more successfully explore and produce data on the grid, consider using Pig and MapReduce. ● WebHCat offers support that you can use to run Hadoop MapReduce (or YARN), Pig, Hive occupations. You can likewise perform Hive metadata tasks utilizing a HTTP (REST style) interface.FLUME A help for Hadoop log streaming. ● A dispersed, trustworthy, and open help called Apache Flume is utilized to rapidly accumulate, gather, and move monstrous measures of streaming information into the Hadoop Distributed File System (HDFS). It is strong and shortcoming lenient, with adjustable dependability strategies for failover and recuperation, and has a

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direct and versatile plan in view of streaming information flows. Apache Flume and different administrations that give natural information into an Enterprise Hadoop bunch are composed by YARN. What Flume Does: Flume enables Hadoop clients to load-balance high-volume streaming data into HDFS. Flume, in example, allows users to: 1. Ingest streaming data from various sources into Hadoop for limit and examination. 2. Support limit stage from transient spikes, when the speed of moving toward data outperforms the rate at which data can stay in contact with the goal. 3. Flume NG uses channel-based trades to guarantee strong message movement. Exactly when a message moves beginning with one expert and then onto the following, two trades are started, one on the expert that conveys the event and the other on the expert that gets the event. This ensures guaranteed movement semantics. 4. To ingest new data streams and additional volume relying upon the circumstance. It aims to take use of one of Flume’s key strengths, which is its ability to consume data from fast streams in the Hadoop Distributed File System (HDFS). Application logs, sensor and machine data, geo-region data, and virtual entertainment are typical sources of these streams. Hadoop allows for the viewing of these many data types for future analysis, including native Apache Hive queries. Then again, they can do what needs to be done, dashboards served by constant data using Apache HBase. In one explicit model, Flume is utilized to log producing activities It produces a log sheet about that run at the point where one run of an item slips off the line. Regardless of whether this happens hundreds or thousands of times each day, the huge volume log document information can stream through Flume into a device for same-day examination with Apache Storm or months or long periods of creation runs can be put away in HDFS and dissected by a quality confirmation engineer utilizing Apache Hive [3]. How Flume Works: 1. a particular piece of data that Flume moves, generally a single log segment) 2. the material that data enters Flume via. Sources can actively look for information or passively anticipate receiving it. Data may be gathered from a variety of sources, such as log4j logs and sys logs. 3. The substance that passes the data on to the goal. Various sinks grant data to be spilled to an extent of complaints. One model is the HDFS sink which creates events to HDFS. 4. the direction between the Sink and the Source. Events are ingested into the channel by sources, and they are excited by sinks. 5. Any actual Java virtual machine running Flume. It is an assortment of sources, sinks, and channels 6. The substance that produces and sends the Event to the Source working inside the Agent.

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Flume components interact in the following way: ● ● ● ● ●

In Flume, a flow begins at the Client. The Client sends the Event to an internal Source of the Agent. The Event is then sent to one or more Channels by the Source that received it. These Channels are drained by one or more Sinks that are part of the same Agent. Channels use the well-known producer–consumer model of data exchange to decouple the intake rate and drain rate. ● The Channel size grows when client-side activity peaks cause data to be created more quickly than the allotted destination capacity can handle. As a result, sources can function normally during the spike. ● One agent’s sink and another agent’s source can be connected by a chain. This chaining makes it possible to build intricate data flow topologies. Since Flume’s appropriated engineering requires no focal coordination point. Every specialist runs autonomously of others with no innate weak link, and Flume can undoubtedly scale on a level plane [7]. Steps Followed for Brand Data Analytics: 1. Start all the services using the start-all.sh command. 2. Then we copy the files dictionary.tsv and time_zone_map.tsv to their respective directories 3. We will now start the flume agent using the following command: flume-ng agent --conf ./conf/ -f /usr/local/flume/conf/flume.conf -Dflume.root.logger=DEBUG,console -n TwitterAgent 4. The hive-serdes-1.0-SNAPSHOT.jar file is now copied into the /usr/local/hive/lib directory. The hive shell will utilize this to get the clean data from the downloaded data and insert it into the hive database. 5. Now we create a file tweets.sql which is on the Desktop. 6. Now run the tweets.sql file using the hive command. 7. Now we look into all the created tables in the hive shell and default database. Pig Hadoop is essentially a high-level programming language that is valuable for exploring huge datasets. Pig hadoop, produced by Yahoo!, is used with Hadoop as often as possible to perform various information organization errands. Pig provides an undeniable programming language called Pig Latin for creating information exploration programs. Pig Latin has a variety of administrators that allow developers to create unique options for composing, viewing, and manipulating information [8, 12–14]. Pig Latin scripts must be written in order to analyse data using Apache Pig. Following that, these scripts must be converted into Map Reduce tasks. The assistance of Pig Engine is used to do this. Features of Pig Hadoop. There are several features of Apache Pig:

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1. n-constructed administrators: Apache Pig gives an excellent arrangement of administrators for playing out a few information tasks like sort, join, channel, and so on. 2. Ease of programming: Since Pig Latin has likenesses with SQL, it is extremely simple to compose a Pig script. 3. Automatic improvement: The undertakings in Apache Pig are naturally advanced. This makes the software engineers focus just on the semantics of the language.

5 Conclusion The customary approach to handling large information is confronting many difficulties as a result of truly expanding volume and computational requests. The focal point of this paper is the difficulties looked by Map Reduce while managing enormous information handling. This work is pointed toward tending to the distinguished difficulties with Map Reduce. By distinguishing difficulties and talking about proposed arrangements or approaches this paper makes ready for further developed arranging of the Big Data undertakings and future exploration.

References 1. Biliris A (1992) An efficient database storage structure for large dynamic objects. IIEEE Data engineering conference, Phoenix, Arizona, pp. 301–308 2. An Oracle White Paper, Hadoop and NoSQL Technologies and the Oracle Database (2011) 3. Cattell (2010) Scalable sql and nosql data stores. ACM SIGMOD Record 39(4):12–27 4. Russom (2011) Big data analytics. TDWI Research 5. Ghemawat S, Gobioff H, Leung S (2003) Google file system 6. Ghemawat S, Wilson C, Hsieh Deborah A, Wallach Burrows M, Chandra T, Fikes A, Gruber R, Chang F, Dean J (2006) Bigtable: a distributed storage system for structured data. OSDI 7. Yang Z, Qichen T, Fan K, Zhu L, Chen R (2008) BoPENG.: performance gain with variable chunk size in GFS-like file systems. J Comput Inf Syst 4(3):1077–1084 8. Madden S (2012) From databases to big data. IEEE Computer Society 9. Dhawan S, Rathee S (2020) Big data analytics using hadoop components like pig and hive. Am Int J Res Sci Technol Eng Mathem 1–5 10. Chaudhary A, Tiwari VN, Kumar A (2014) Design an anomaly based fuzzy intrusion detection system for packet dropping attack in mobile ad hoc networks. IEEE International Advance Computing Conference (IACC). IEEE 11. Chaudhary A, Tiwari VN, Kumar VN (2016) A new intrusion detection system based on soft computing techniques using neuro-fuzzy classifier for packet dropping attack in manets. Int J Netw Sec 18(3):514–522 12. Chaudhary A, Kumar A, Tiwari VN (2014) A reliable solution against packet dropping attack due to malicious nodes using fuzzy logic in MANETs. International Conference on Reliability Optimization and Information Technology (ICROIT), IEEE 13. Weets Jean-Francois, Kakhani MK, Kumar A (2015) Limitations and challenges of HDFS and MapReduce. 2015 International Conference on Green Computing and Internet of Things (ICGCIoT). IEEE

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14. Srivastava P, Goyal S, Kumar A (2015) Analysis of various NoSql database.2015 International Conference on Green Computing and Internet of Things (ICGCIoT). IEEE Computer Society 15. Goyal S, Srivastava P, Kumar A (2015) An overview of hybrid databases. 2015 International Conference on Green Computing and Internet of Things (ICGCIoT). IEEE

The λNF Problem .

Anurag Dutta

and DeepKiran Munjal

Abstract The Dutch National Flag problem—DNF Problem, a Computational Problem proposed by the Dutch Computer Scientist—Edsger Wybe Dijkstra—brought a new edge to the domains of Sorting. In this work, we would extend Dijkstra’s Problem to a generalized version, where we would be considering flags with variable numbers of colours and will try to devise an algorithm to arrange them in a monotonic order by its magnitude. Keywords Dnf sort · Computational complexity · Partitioning · Sorting algorithms

1 Introduction The Dutch National Flag problem [1] is a Computational Problem relating to the flag of the Netherlands, which has 3 colours inside it—RED, WHITE, and BLUE. Problem Statement: Given an unordered list .L (n) = {α0 , α1 , α2 , α3 , . . . , αn−1 }, a permutation of the list .L (n) is to be found such that .αi ≤ α j ∀ j > i given that .αi = {β, γ, δ} ∀ i ≥ 0 and .β / = γ / = δ. Here, .β, .γ, .δ could be considered as a correspondence to the Colours of the Dutch National Flag (Fig. 1).

A. Dutta (B) Department of Computer Science and Engineering, Government College of Engineering and Textile Technology, Serampore, India e-mail: [email protected] D. Munjal Department of Computer Applications, G L Bajaj Institute of Technology and Management, Greater Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_28

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Fig. 1 The Dutch National Flag

1.1 Naïve Solution We could just take control of the problem by making use of the generic sorting algorithms, like Merge Sort [2]. We could Sort the entire list, .L (n) and this would fetch us the required permutation [3] where .αi ≥ α j ∀ j > i or .αi ≤ α j ∀ j > i. But the computational complexity [4] of the solution isn’t catchy. I mean, we could even hit the bottom more. The Computational Time Complexity of the Algorithm is of the order .nlog2 n. The Computational Space Complexity of the Algorithm is of the order .n. The pseudocode [5] for this Naïve Solution is given in Algorithm 1. But we could definitely be better. Algorithm 1 Naïve solution to the DNF problem Require: L (n) = {α0 , α1 , . . . , αn−1 } ∋ αi = {β, γ, δ} ∀ i ≥ 0 Ensure: A Permutation of the list L (n) ∋ αi ≤ α j ∀ j > i 1: function Naïve Solution(L (n)) 2: SORT(L (n)) 3: end function

1.2 Hashing We could just traverse the whole list .L (n), and store the count [6] of .β, .γ, and .δ. Following that, we could just make the whole list thrive by establishing a comparison between .β, .γ, and .δ, on the basis of their magnitude. The Computational Time of the Algorithm turns out to be of the order .n. The Computational Space Complexity of the Algorithm is of the order .n. The pseudocode for this solution is given in

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Algorithm 2. But can we be better? Definitely, and that’s too by making use of Dijkstra’s solution. Algorithm 2 Solution to the DNF problem by hashing Require: L (n) = {α0 , α1 , . . . , αn−1 } ∋ αi = {β, γ, δ} ∀ i ≥ 0 Ensure: A Permutation of the list L (n) ∋ αi ≤ α j ∀ j > i 1: Let L' (n) be a new list 2: function Hashing(L (n)) 3: x ← COUNT(min(β, γ, δ) in L (n)) 4: y ← COUNT(max(β, γ, δ) in L (n)) 5: w ← sum(β, γ, δ) − min(β, γ, δ) − max(β, γ, δ) 6: z ← COUNT(w in L (n)) 7: k←0 8: for i = 0 to x do L' i ← min(β, γ, δ) 9: 10: k ←k+1 11: end for 12: for i = k to z do L' i ← w 13: 14: k ←k+1 15: end for 16: for i = k to y do L' i ← max(β, γ, δ) 17: 18: k ←k+1 19: end for L (n) = L' (n) 20: 21: end function

1.3 Dijkstra’s Solution The solution is an approach to sort the list in place. It performs 4 partitions in the list internally, by making use of three pivots, namely low, mid, and high, initiating at the indices .0, 0, n − 1, respectively [7]. A Comparative Metric with the invariance, .low ≤ mid ≤ high, will be indulged as 1. If .Lmid = min(β, γ, δ), we perform in place a swap between .Llow and .Lmid . With that, we will increment both .low and .mid. 2. If .Lmid = sum(β, γ, δ) − min(β, γ, δ) − max(β, γ, δ), we will simply increment .mid. 3. If .Lmid = max(β, γ, δ), we perform in place a swap between .Lhigh and .Lmid . With that, we will decrement .high. The pseudocode for this solution is given in Algorithm 3.

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Algorithm 3 Solution to the DNF Problem by Dijkstra Require: L (n) = {α0 , α1 , . . . , αn−1 } ∋ αi = {β, γ, δ} ∀ i ≥ 0 Ensure: A Permutation of the list L (n) ∋ αi ≤ α j ∀ j > i 1: function Dijkstra’s Solution(L (n)) 2: low ← 0 3: mid ← 0 4: high ← n − 1 5: while mid ≤ high do 6: if Lmid = min(β, γ, δ) then 7: swap(Llow , Lmid ) 8: low ← low + 1 9: mid ← mid + 1 10: else if Lmid = sum(β, γ, δ) − min(β, γ, δ) − max(β, γ, δ) then 11: mid ← mid + 1 12: else if Lmid = max(β, γ, δ) then 13: swap(Lhigh , Lmid ) 14: high ← high − 1 15: end if 16: end while 17: end function

2 The Indian National Flag Problem In accordance with the Dutch National Flag problem, we instantiate the Indian Flag problem. The Indian National Flag has 4 colours inside it—SAFFRON, WHITE, GREEN, and NAVY BLUE (Fig. 2). Problem Statement:Given an unordered list .L (n) = {α0 , α1 , α2 , α3 , . . . , αn−1 }, a permutation of the list .L (n) is to be found such that .αi ≤ α j ∀ j > i given that .αi = {β, γ, δ, ε} ∀ i ≥ 0 and .β /= γ /= δ /= ε. Here, .β, .γ, .δ, and .ε could be considered as a correspondence to the Colours of the Indian National Flag.

Fig. 2 The Indian National Flag

The .λNF Problem

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The problem can be solved via place swaps. The algorithm corresponding to that would be the extension of Dijkstra’s solution of the DNF. Firstly, we would separate the portions having .min(β, γ, δ, ε) and.max(β, γ, δ, ε) at the front and the rear ends, respectively. A comparison is made between the 2 remaining parameters, and they are assigned their respective portions based on their order. Before we proceed further, let us assume for the sake of simplicity that the quadruple, .(β, γ, δ, ε), is arranged as per increasing order of its magnitude, i.e., .β < γ < δ < ε. The pseudocode for this solution is given in Algorithm 4. Algorithm 4 Solution to the INF problem Require: L (n) = {α0 , . . . , αn−1 } ∋ αi = {β, γ, δ, ε} ∀ i ≥ 0 Ensure: A Permutation of the list L (n) ∋ αi ≤ α j ∀ j > i 1: function Inf(L (n)) 2: low ← 0 3: mid ← 0 4: high ← n − 1 5: while mid ≤ high do 6: if Lmid = β then 7: swap(Llow , Lmid ) 8: low ← low + 1 9: mid ← mid + 1 10: else if Lmid = ε then 11: swap(Lhigh , Lmid ) 12: high ← high − 1 13: else 14: mid ← mid + 1 15: end if 16: end while 17: while low ≤ high do 18: if Llow = γ then 19: low ← low + 1 20: else 21: swap(Lhigh , Llow ) 22: high ← high − 1 23: end if 24: end while 25: end function

3 The Zimbabwean National Flag Problem In accordance with the Dutch National Flag problem, we instantiate the Zimbabwean Flag problem. The Zimbabwean National Flag has 5 colours inside it—GREEN, GOLD, RED, BLACK, and WHITE (Fig. 3). Problem Statement: Given an unordered list .L (n) = {α0 , α1 , α2 , α3 , . . . , αn−1 }, a permutation of the list .L (n) is to be found such that .αi ≤ α j ∀ j > i given that

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Fig. 3 The Zimbabwean National Flag

αi = {β, γ, δ, ε, ζ} ∀ i ≥ 0 and .β /= γ /= δ /= ε /= ζ. Here, .β, .γ, .δ, .ε, and .ζ could be considered as a correspondence to the Colours of the Zimbabwean National Flag. The problem can be solved via place swaps. The algorithm corresponding to that would be the extension of Dijkstra’s solution of the DNF. Firstly, we would separate the portions having .min(β, γ, δ, ε, ζ) and .max(β, γ, δ, ε, ζ) at the front and the rear ends, respectively. A comparison is made between the 3 remaining parameters, and are assigned their respective portions based on their order. Before we proceed further, let us assume for the sake of simplicity that the pentuple, .(β, γ, δ, ε, ζ), is arranged as per increasing order of its magnitude, i.e., .β < γ < δ < ε < ζ. The pseudocode for this solution is given in Algorithm 5.

.

4 The National Flag with .λ Colours In this section, we would be generalizing the National Flag problem. For that, we would consider a flag with .λ distinct colours—.C1 , C2 , C3 , . . . , Cλ . Problem Statement: Given an unordered list .L (n) = {α0 , α1 , α2 , α3 , . . . , αn−1 }, a permutation of the list .L (n) is to be found such that .αi ≤ α j ∀ j > i given that .αi = {C 1 , C 2 , C 3 , . . . , C λ } ∀ i ≥ 0 and .C 1 / = C 2 / = C 3 / = ... / = C λ . Here, .C 1 , C 2 , C 3 , . . . , C λ could be considered as a correspondence to the Colours of the National Flag. The problem can be solved via place swaps, as we mentioned in Sects. 1–3. The algorithm targeting | | this problem can be quite easily, but at the Computational Cost of the order . λ2 × n, though with no additional space. In the Worst Case, this .λNF problem can be accounted for when .λ = n, i.e., all the elements of the list .L (n) being distinct. In the Best Case, this .λNF problem can be accounted for to be intuited accordingly.

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Algorithm 5 Solution to the ZNF problem Require: L (n) = {α0 , . . . , αn−1 } ∋ αi = {β, γ, δ, ε, ζ} ∀ i ≥ 0 Ensure: A Permutation of the list L (n) ∋ αi ≤ α j ∀ j > i 1: function Inf(L (n)) 2: low ← 0 3: mid ← 0 4: high ← n − 1 5: while mid ≤ high do 6: if Lmid = β then 7: swap(Llow , Lmid ) 8: low ← low + 1 9: mid ← mid + 1 10: else if Lmid = ζ then 11: swap(Lhigh , Lmid ) 12: high ← high − 1 13: else 14: mid ← mid + 1 15: end if 16: end while 17: mid ← low 18: while mid ≤ high do 19: if Lmid = γ then 20: swap(Llow , Lmid ) 21: low ← low + 1 22: mid ← mid + 1 23: else if Lmid = ε then 24: swap(Lhigh , Lmid ) 25: high ← high − 1 26: else 27: mid ← mid + 1 28: end if 29: end while 30: end function

5 Conclusion To conclude, the .λNF problem could be like a Sorting Problem, in which we are given a list of numbers with .λ distinct entries. It has been shown inductively in the work that the minimal time, the .λNF Sort can account is totally dependent on the 2 value of .λ. If all the entries are distinct, it will account for a time complexity of . n2 . Below is an Inductive Proof to the Claim, accounted for by the author. | | Claim: The Computational Time Complexity of the .λNF problem is . λ2 × n ∀ λ = (0, n]. Proof Let . Pλ be| the | proposition that the Computational Time Complexity of the λNF problem is . λ2 × n.

.

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Base Case the proposition, . P1 , the ComLet us consider the base case, .λ = 1. By considering | | putational Complexity is ought to be 0, as . 21 = 0, which seems to fit completely; as in a list which is having a single variety of entry, sorting is Irrelevant. Induction Let us consider that every instance of the proposition, . P1 , P2 , . . . , Pn−1 is true. Strong Induction We need to conclude that . Pn is true. The proposition, . Pn , states that all the entries in the list, .L (n), are distinct. In the work, “Validation of Minimal Worst-Case Time Complexity by Stirling’s, Ramanujan’s, and Mortici’s Approximation” by Dutta, and Roy Choudhury [8], sorting was sought in terms of searching. According to that, Linear Search was used, . Pn = n + (n − 1) + (n − 2) + (n − 3) + · · · + 1. It 2 is quite understandable from the proposition, . Pn , that it is of the order . n2 . Hence, we |could | conclude that the Computational Time Complexity of the .λNF problem is . λ2 × n.

References 1. Dutch National Flag problem and algorithm. http://www.csse.monash.edu.au/~lloyd/ tildeAlgDS/Sort/Flag/. Last accessed 24 Oct 2022 2. Katajainen J, Träff J (1997) A meticulous analysis of Mergesort programs. In: Lecture notes in computer science proceedings of the 3rd Italian conference on algorithms and complexity, LNCS, vol 1203. Springer, Heidelberg, pp 217–228. https://doi.org/10.1007/3-540-62592-5_74 3. Scheinerman EA (2012) Mathematics: a discrete introduction, 3rd edn. Cengage Learning, Boston, United States 4. Sipser M (2006) Introduction to the theory of computation, 2nd edn. Thomson Course Technology, England 5. Zobel J (2013) Writing for computer science, 2nd edn. Springer, Heidelberg 6. Ramakrishna MV, Zobel J (1997) Performance in practice of string hashing functions. Database Syst Adv Appl 97:215–223 7. Dijkstra EW (1976) A discipline of programming, 1st edn. Prentice Hall Inc., Englewood Cliffs 8. Dutta A, Roy Choudhury M (2022) Validation of minimal worst-case time complexity by Stirling’s, Ramanujan’s, and Mortici’s approximation. In: 2022 3rd international conference for emerging technology (INCET) on IEEE Xplore. IEEE, India, pp 1–5. https://doi.org/10.1109/ INCET54531.2022.9824687

The Indian Search Algorithm Anurag Dutta

and Pijush Kanti Kumar

Abstract Searching algorithms deal with searching for key .K in a heap of data. In this work, an indigenous Searching Algorithm—“The Indian Search Algorithm” has been proposed that will be efficient enough to search for a key in the order log⎛

⎞ (n

.

⎜ ⎜ ⎜ ⎜ ⎜ 1 ⎜k+ 1 ⎜ k+ 1 k+ ⎜ 1 k+ ⎜ k+ 1 ⎝

..

− 1) + log2 (n)

.

⎞

⎛ ⎜ ⎜ ⎜ where .⎜k + ⎜ ⎝

⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠

1 k+

1 k+

k+

1 1 k+ 1

⎟ ⎟ ⎟ ⎟ is the .kth metallic ratio. The Search Algorithm would be ⎟ ⎠

.. . an efficient one at least for an Ordered List. In the paper, we have tried to incorporate two children from the same hierarchy—with the parent being the Generic Search with .kth metallic ratio, which are Indo-Pellian Search, following the Pell Series, and Indo-Fibonaccian Search following the Fibonacci Series. The Computational Complexity of both have been evaluated. Keywords Metallic ratio · Searching · Divide and conquer · Pell’s numbers · Fibonacci’s numbers

A. Dutta (B) Department of Computer Science and Engineering, Government College of Engineering and Textile Technology, Serampore, India e-mail: [email protected] P. Kanti Kumar Department of Information Technology, Government College of Engineering and Textile Technology, Serampore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_29

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1 Introduction For searching an ordered list, there exist numerous techniques, one of them being the binary search, which improvises its search and decreases the search space by slicing the list in halves, and is repeated until the target is hit. Now, in this work, we have developed a new type of algorithm, built upon the D&C Technique. In information science, divide and triumph over is a set of rules of layout paradigm. A divide-and-triumph over set of rules recursively divide a problem into extra associated or comparable sub-problems till it is easy to be solved directly, then integrate the answers of the sub-problem to get the answer of the authentic problem. The algorithm 1. Describes the set of partitions as a term of the Recursively Enumerable Series, specially with series holding a metallic ratio [1] between consecutive terms for series with higher cardinality, like Fibonacci Series [2], Lucas Series [3, 4], etc. 2. Next it performs a check on either side of the partitions. 3. Finally, the partition with the key being possibly encapsulated within is selected and further searched through, by making use of Binary Search [5]. In the next section, we would draw attention on applying the Indian Search Algorithm subjected to selection of specific recursively enumerable series. In Sect. 2, we would demonstrate the working of the Indian Search Algorithm, taking Pell Numbers as a basis of Partitions, while in Sect. 3, we preferably chose Fibonacci Numbers as the Partition Basis. The Computational Complexity subjecting to these selected series is thoroughly studied, in their respective sections, and a rough Upper Bound Estimate [6] of each of them has been proposed. In practice, it is believed to perform much better [7] than it promises to be. Finally, a Conclusion have been drawn in Sect. 4 regarding a Generic Computational Complexity, with consideration of any Recursively Enumerable Series with existence of Metallic Ratio between consecutives.

2 Indo-Pellian Search In mathematics, the ancient Pell numbers are the infinite sequences of integers that form the denominator of the closest rational approximation to the square root of 2. Pell Series are the ones following the rule: u =

. n

⎧ ⎨

0 n=0 1 n=1 ⎩ 2u n−1 + u n−2 otherwise

(1)

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The closed form of this series can be represented as ⎛⎛

⎜⎜ ⎟ ⎜⎜ ⎟ ⎜⎜ ⎟ 1 ⎜ ⎜2 + 2+ ⎟ 1 ⎜⎜ 1 ⎟ 2+ 1 ⎜⎝ 2+ ⎠ 2+ 1 ⎜ ⎜ .. ⎜ . ⎛ .u n = ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜2 + 1 1 ⎜ ⎜ 2+ 1 ⎜ 2+ 1 2+ ⎝ ⎝ 2+ 1 .. .

⎞⎞n ⎞

⎛ ⎛

⎞n

⎜ ⎜ ⎜ ⎜ ⎜ ⎜ − ⎜C ⎜2 + ⎜ ⎜ ⎝ ⎝

1 2+

1 2+

2+

1 1 2+ 1

..

⎛

⎞

⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎟ − C ⎜2 + ⎜ ⎟ ⎝ ⎠

⎟⎟ ⎟⎟ ⎟⎟ ⎟⎟ ⎟⎟ ⎠⎠ . ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠

1 2+

1 2+

2+

1 1 2+ 1

..

⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠

(2)

.

where .C(.) is the Conjugate Operation [8]. A few terms of the Pell Series are u = 0, 1, 2, 5, 12, 29, 70, 169, 408, 985, 2378, 5741, 13860, . . . Let us have the list, .L = α0 , α1 , α2 , α3 , . . . , αn−1 and .u = {u 0 , u 1 , u 2 , u 3 , . . . , u n−1 , . . . , ∞}. We will have to choose such a value of .i such that .u i ≤ n − 1.

.

Considering .n → ∞, .i ≤ 1.18 × log⎛

⎜ ⎜ ⎜ ⎜ ⎜ 1 ⎜2+ 1 ⎜ 2+ 1 2+ ⎜ 1 2+ ⎜ 2+ 1 ⎝

⎞ (n ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠

− 1).

.. . Now, we spawn all values of.u k ∀ k ≥ 0 & k ≤ i. These sets of.u k ∀ k ≥ 0 & k ≤ i will be the set of partitions [9]. We will now consider these .i + 1 values as the pivots, and start narrowing down our search space [10]. Finally, we would reach out [an interval ] where the key, .K, to be searched may be found. Let that interval . u ξ , u ξ +1 . Now, we would perform binary search in that interval. Consider be ( ) . u ξ +1 − u ξ → n. So, we could conclude the worst-case time complexity of this searching technique would be ⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎛ .T (n) ≤ ⎜log ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ 1 ⎜2+ ⎜ ⎜ 1 2+ 1 ⎜ ⎜ 2+ ⎜ 1 2+ ⎝ ⎜ 2+ 1 ⎝ .. .

⎞

⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠

⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎞ (n − 1)⎟ + log (n) ⎟ 2 ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ ⎠

( √ ) 2 2 × log⎛

Which can be further delineated as

⎜ ⎜ ⎜ ⎜ ⎜ 1 ⎜2+ 1 ⎜ 2+ 1 2+ ⎜ 1 2+ ⎜ 2+ 1 ⎝

..

.

(3)

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⎞

⎛

⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ( √ ) ⎟ ⎜ ⎜ log 2 2 × log(n − 1) ⎟ ⎟ ⎜ ⎛ ⎞⎟ .T (n) ≤ ⎜ ⎟ + log2 (n) ⎜ ⎟ ⎜ ⎜ ⎜ ⎟⎟ ⎜ ⎟⎟ ⎜ 2⎜ ⎟⎟ 1 ⎜ log ⎜ 2 + ⎜ ⎟⎟ 1 2+ ⎜ 1 ⎜ ⎟⎟ 2+ 1 ⎝ 2+ ⎝ ⎠⎠ 2+ 1 .. .

(4)

3 Indo-Fibonaccian Search Fibonacci Series are the ones following the rule: u =

. n

⎧ ⎨

0 n=0 1 n=1 ⎩ u n−1 + u n−2 otherwise

(5)

Fibonacci numbers were first described in Indian mathematics in 200 BC. Pingala’s work [11] on an enumeration of Sanskrit poetic patterns may be formed from syllables of two degrees in length. They are named after the Italian mathematician Leonardo of Pisa, later known as Fibonacci rice field. He introduced number sequences into Western European mathematics in 1202 in his Book of the Soroban [12]. The closed form of this series can be represented as ⎛⎛

⎜⎜ ⎟ ⎜⎜ ⎟ ⎜⎜ ⎟ 1 ⎜ ⎜1 + 1+ ⎟ 1 ⎜⎜ 1 ⎟ 1+ 1 ⎜⎝ 1+ ⎠ 1+ 1 ⎜ ⎜ .. ⎜ . ⎛ .u n = ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜1 + 1 1 ⎜ ⎜ 1+ 1 ⎜ 1+ 1 1+ ⎝ ⎝ 1+ 1 .. .

⎞⎞n ⎞

⎛ ⎛

⎞n

⎜ ⎜ ⎜ ⎜ ⎜ ⎜ − ⎜C ⎜1 + ⎜ ⎜ ⎝ ⎝ ⎞

1+

1 1+

1+

1 1 1+ 1

..

⎛

⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎟ − C ⎜1 + ⎜ ⎟ ⎝ ⎠

⎟⎟ ⎟⎟ ⎟⎟ ⎟⎟ ⎟⎟ ⎠⎠

1

. ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠

1 1+

1 1+

1+

1 1 1+ 1

..

⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠

(6)

.

where .C(.) is the Conjugate Operation. A few terms of the Fibonacci Series are u = 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, . . .. Fibonacci numbers pop up in math so often in unexpected ways that there’s an entire journal dedicated to research called the Fibonacci Quarterly [13]. Applications of Fibonacci numbers include computer algorithms such as the Fibonacci search

.

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technique [14] and the Fibonacci heap data structure [15–19], and graphs known as Fibonacci blocks used to connect parallel and distributed systems. They also occur in biomes, tree branching, leaf arrangement on stems, pineapple shoots, artichoke flowers, spreading ferns, and pine cone bracts. Let us have the list, .L = {α0 , α1 , α2 , α3 , . . . , αn−1 } and .u = {u 0 , u 1 , u 2 , u 3 , . . . , u n−1 , . . . , ∞}. We will have to choose such a value of .i such that .u i ≤ n − 1. ⎞ (n − 1). Considering .n → ∞, .i ≤ 1.67 × log⎛ ⎜ ⎜ ⎜ ⎜ ⎜ 1 ⎜1+ 1 ⎜ 1+ 1 1+ ⎜ 1 1+ ⎜ 1+ 1 ⎝

⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠

.. . Now, we spawn all values of .u k ∀ k ≥ 0 & k ≤ i. These sets of .u k ∀ k ≥ 0 & k ≤ i will be the set of partitions. We will now consider these.i + 1 values as the pivots, and start narrowing down our search space. Finally, we would reach[out an interval where ] the key, .K to be searched may be found. Let that interval( be . u ξ , u ξ)+1 . Now, we would perform binary search in that interval. Considering . u ξ +1 − u ξ → n. So, we could conclude the worst-case time complexity of this searching technique would be ⎞

⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎛ .T (n) ≤ ⎜log ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ 1 ⎜1+ ⎜ ⎜ 1 1+ 1 ⎜ ⎜ 1+ ⎜ 1 1+ ⎝ ⎜ 1+ 1 ⎝ .. .

⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠

⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎞ (n − 1)⎟ + log (n) ⎟ 2 ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ ⎠

(√ ) 5 × log⎛

which can be further delineated as ⎛

⎜ ⎜ ⎜ ⎜ ⎜ 1 ⎜1+ 1 ⎜ 1+ 1 1+ ⎜ 1 1+ ⎜ 1+ 1 ⎝

..

.

(7)

⎞

⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ (√ ) ⎜ ⎟ ⎜ log 5 × log(n − 1) ⎟ ⎜ ⎟ ⎞⎟ ⎛ .T (n) ≤ ⎜ ⎜ ⎟ + log2 (n) ⎜ ⎟ ⎜ ⎟⎟ ⎜ ⎜ ⎟ ⎟ ⎜ ⎜ 2⎜ ⎟⎟ 1 ⎜ log ⎜1 + ⎟ ⎟ 1 1+ ⎜ 1 ⎟⎟ ⎜ 1+ 1 ⎝ ⎠ 1+ ⎠ ⎝ 1+ 1 .. .

(8)

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Fig. 1 Comparative plot accounting the Indian Fibonacci, Pell search algorithm, and the linear search

1000

Indo Fibonaccian Search Linear Search Indo Pellian Search

T (n)

800 600 400 200 0 0

200

400

n

600

800

1000

4 Conclusion To conclude, the Indian Search Algorithm is at an edge over the traditional, naive Linear Search [20]. The comparative plot resembling that could be clearly seen from Fig. 1. It could also be seen that the Indo-Pellian Search promises to work well with larger datasets than Indo-Fibonaccian Search. Well, it could be concluded that any Recursively Enumerable Series with the ∀k ≥ 1 between terms consecutively existence of Metallic Ratio, .k + k+ 1 1 k+

k+

1 1 k+ 1

.. . will be having the computational complexity of the order .λ + log2 (n), where the value of .λ would be ⎞ ⎞⎞ ⎛ ⎛ ⎛⎛ ⎞ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ log⎜ k+ ⎜ ⎜ ⎜⎜ ⎜ ⎝⎜ ⎝ ⎜ ⎜ ⎜ .⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝

⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ − C ⎜k + ⎟ ⎜ ⎠ ⎝

⎟⎟ ⎟ ⎟⎟ ⎟ ⎟⎟ ⎟⎟ × log(n − 1) ⎟ 1 1 ⎟ k+ k+ 1 1 ⎟⎟ k+ k+ ⎟ 1 1 k+ k+ ⎠⎠ ⎟ k+ 1 k+ 1 ⎟ .. .. ⎟ . ⎛ . ⎟ ⎞ ⎟ ⎟ ⎟ ⎜ ⎟ ⎟ ⎜ ⎟ ⎟ ⎟ 1 2⎜ ⎟ log ⎜k + k+ ⎟ 1 ⎟ 1 ⎜ ⎟ k+ 1 ⎠ k+ ⎝ ⎠ k+ 1 .. . 1

1

As the golden ratio relates to the pentagon, the silver ratio relates to the octagon. Just as the golden ratio is related to Fibonacci numbers, the silver ratio is related to Pell numbers and the bronze ratio is related to OEIS: A006190. All Fibonacci numbers are the sum of the previous number plus 1, all Pell numbers are the sum of the

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previous number multiplied by 2 and the number before it, and all Bronze Fibonacci numbers are the sum of the previous numbers multiplied the previous number by 3. If we ratio successive Fibonacci numbers, their ratios will approach the Golden Mean, the proportions of Pell numbers will approach the Silver Mean, and the proportions of Bronze Fibonacci Numbers will approach the Bronze Mean. Though, it could be verified that, as .k → ∞, .T (n) → log2 (n). Future works could be encompassed around showing the practical time that these algorithms demand, and if possible try to propose a constant, .Ʌ such that Ʌ=

.

max (T (n)Practical , T (n)Theoretical ) min (T (n)Practical , T (n)Theoretical )

Possible reasons for this inflation could be briefly discussed. Also, this search could be used as an intermediate bridge for the Indian Sorting Algorithm, which is believed to be more efficient than the Quadratic Sorting Algorithms.

References 1. de Spinadel VW (1999) The family of metallic means. Vis Math 1(3) 2. Singh P (1985) The so-called Fibonacci numbers in ancient and medieval India. Hist Math 12(3):229–244. https://doi.org/10.1016/0315-0860(85)90021-7 3. Swinton J, Ochu E (2016) Novel Fibonacci and non-Fibonacci structure in the sunflower: results of a citizen science experiment. R Soc Open Sci 3(5):160091. https://doi.org/10.1098/ rsos.160091 4. Pratsiovytyi M, Karvatsky D (2017) Jacobsthal-Lucas series and their applications 5. Williams LF (1976) A modification to the half-interval search (binary search) method. In: Proceedings of the 14th annual southeast regional conference on—ACM-SE 14. https://doi. org/10.1145/503561.503582 6. (1894) Die analytische Zahlentheorie. Teubner 7. Andoni A, Razenshteyn I, Nosatzki NS (2017) LSH forest: practical algorithms made theoretical. In: Proceedings of the twenty-eighth annual ACM-SIAM symposium on discrete algorithms. https://doi.org/10.1137/1.9781611974782.5 8. Friedberg SH, Insel AJ, Spence LE (2018b) Linear algebra. Pearson 9. Andrews GE (1998) The theory of partitions. Cambridge University Press 10. Amir O, Tyomkin L, Hart Y (2022) Adaptive search space pruning in complex strategic problems. PLoS Comput Biol 18(8):e1010358. https://doi.org/10.1371/journal.pcbi.1010358 11. Plofker K (2009) Mathematics in India. Princeton University Press 12. Leonardus, Fibonacci L, Pizy L, Sigler L (2002) Fibonacci’s Liber Abaci: a translation into modern English of Leonardo Pisano’s book of calculation. Springer Publishing 13. The Fibonacci Quarterly (n.d.) https://www.fq.math.ca/ 14. Ferguson DE (1960) Fibonaccian searching. Commun ACM 3(12):648. https://doi.org/10. 1145/367487.367496 15. Introduction to Algorithms (2001) MIT Press, MA 16. Fredman ML, Tarjan RE (1987) Fibonacci heaps and their uses in improved network optimization algorithms. J ACM 34(3):596–615. https://doi.org/10.1145/28869.28874 17. Fredman ML, Sedgewick R, Sleator DD, Tarjan RE (1986) The pairing heap: a new form of self-adjusting heap. Algorithmica 1(1–4):111–129. https://doi.org/10.1007/bf01840439 18. Brodal GS, Lagogiannis G, Tarjan RE (2012) Strict Fibonacci heaps. In: Proceedings of the 44th symposium on theory of computing—STOC’12. https://doi.org/10.1145/2213977.2214082

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19. Mrena M, Sedlacek P, Kvassay M (2019) Practical applicability of advanced implementations of priority queues in finding shortest paths. In: 2019 international conference on information and digital technologies (IDT). https://doi.org/10.1109/dt.2019.8813457 20. Shi Z, Jin C (2022) Linear attacks On SNOW 3G and SNOW-V using automatic search. Comput J. https://doi.org/10.1093/comjnl/bxac012

COVID-19’s Influence on Buyers and Businesses John Harshith and Eswar Revanth Chigurupati

Abstract The coronavirus disease spread has had a lasting global economic impact as companies and governments try to pay for testing and containment procedures. These are necessary steps to limit and reduce the hazard to people’s lives and to mitigate any danger of long-term consequences on economies. The latest epidemic caused a scare among Boeing and Airbus where many people couldn’t travel to high-risk countries, which affects their clientele. The authors use ARIMA model for forecasting the employment rate changes and mobility changes in this research. They start by selecting economic variables (covariates) that may be linked to consumer expenditure and firm revenue. Then, in order to eliminate uncorrelated or slackly correlated covariates, they perform a dimensionality reduction procedure utilizing Gibbs sampling. Keywords Time series forecasting · ARIMA model · Machine learning · Dimensionality reduction

1 Introduction COVID-19 is dramatically changing the world, both socially and economically. Many cities are flustering under the pandemic, while some are not so much affected. In this paper, we present a methodology for studying the economic impact of COVID-19 on cities under a consumer spending and business revenue perspective. In particular, we focus on how COVID-19 impacts domestic cities and do not take cities outside America into account.

J. Harshith (B) VIT University, Vellore, India e-mail: [email protected] E. R. Chigurupati Jawaharlal Nehru Technological University, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_30

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As the single most important indicator for economic activities, GDP consists of consumer spending, government expenditure, imports and exports, and business revenue. Among them, we will assume that government expenditure is uniform across most cities, which makes sense because even if differences do exist, its influence would be overshadowed by changes in the other three parts [1]. Also, we do not investigate the influence of imports and exports as we focus our attention on domestic situations. Consumer spending and business revenue will be the protagonist of our analysis, as they are closely related to the well-being of cities [7]. In this paper, we first choose economic factors (covariates) that are possibly correlated with consumer spending and business revenue. Then, we run a dimensionality reduction process with Gibbs sampling to remove uncorrelated or loosely correlated covariates. Furthermore, we conduct time series forecast for the remaining economic factors in selected regions. Ultimately, we train a model with neural network, providing the predictions for the covariates as input, and obtain predictions for how much cities will be affected in the following year, which will also shed light on which type of cities would be most acutely influenced.

2 Literature Review Research has not progressed to a degree where time series for particular disciplines can be forecasted; however, numerical methods do show some promise in evaluating time series. Review of the literature has shown that the major research limitation is that there have been studies on forecasting time series datasets only to certain relevant fields such as economics or psychology—while being absent in other fields [5]. The linear regression model is commonly employed as the first model in time series to address numerous aspects with its applicability (noticeably, it comprises direct response’s incidental subsets). Although the coefficient vector of a Kalman filter can identify likely values that are not otherwise constrained due to persistence or cumulative effects, its indication on changes in the corresponding lag may be misleading [6]. A systems model can be representation for inference on many length feedback sequences by estimating natural parameters, but this introduces an additional back approximation when estimation length surpasses processing length [1]. Traditional parametric models for time series forecasting must make assumptions about the level of independence between pairs of predictors that are included in the model equations. Hence, when data exhibits over-dependence (i.e., autocorrelation) among predictors there arises certain limitations in forecasting performance arising from errors not accounted for in these traditional models’ parameters or functions known as “intrinsic forecast uncertainty” [9]. This uncertainty presents a dilemma in who exactly should be responsible for review of the statistical reports produced in different agencies [8]. Especially considering this ambiguity over what the true influence would be on consumers from these COVID forecasts; later influencing sentiment on stock prices in both short-term and long-term scales [5].

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3 Choice of Covariates and Dimensionality Reduction Consumer spending and business revenue depend on a lot of economic factors, and it’s hard to directly predict their trends in the future. Therefore, we will consider them as dependent variables and choose a number of independent variables that are easier to apply time series forecast on, and then establish relationship between them [6].

3.1 Choice of Covariates In particular, we propose the following possible covariates, or independent variables: change in rate of employment for low-income, middle-income, and high-income workers, respectively, income level, population, the cumulative number of cases, the number of new cases and population mobility. In total, there are eight covariates. We split the change in the rate of employment into three covariates because the rate of change varies significantly for these three types of workers [10].

3.2 Dimensionality Reduction Then, we run a dimensionality reduction process with Gibbs sampling using continuous spike prior. There is a set of dimensions which together describe a highdimensional mixed data model. In order to avoid overfitting, we usually need to reduce the dimensions, but this can result in incoherent groups of points. Gibbs sampling is one of the dimension reduction algorithms that offers improved quality while recording singularities. The premise behind this algorithm lies in searching for paths within the space that primarily represent similar attributes, clustering, and assessing evidence within these paths in an exploratory way. This minimizes redundancy among different dimensions while ensuring areas of indistinguishability are not vastly underrepresented or eliminated altogether. In particular, we use student t-distribution as prior because it provides heavier tails and thus more similar to a Dirac spike prior, which is typically used for variable selection. The result gives that the cumulative number of COVID cases, income level, and population have little predictive value for change in consumer spending, while population and income level has little predictive value for change in business revenue. Thus, we will not take population and income value into account in the following sections and focus our attention on the remaining six covariates [3].

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4 Time Series Forecast for Economic Factors in Selected Regions Forecasting is a crucial aspect of our everyday lives. The forecasting of prices and trends is integral to the stock market as well as predictive analysis of weather systems which allow us to make decisions on when should I buy an umbrella or which clothes to pack for future trips. Time series forecasting generally uses linear regression models that forecast features based on past relationships with those features in order to predict the series’ future values. Though univariate linear regression creates forecasts that aren’t well represented by the original data, non-Linear analysis takes these natural deviations in a series into account and therefore provides stronger forecasts than with traditional linear regression. To illustrate the economic impacts of COVID-19 across different regions in the United States, we select Los Angeles (CA), Chicago (IL), and Charlotte (NC) and perform time series forecast on their rates of employment across income levels and their residents’ mobility, both of which are crucial independent variables indicating their respective economic status [4]. These three cities are chosen because they are scattered broadly across the US and thus represent vastly different types of cities and because their data of employment rate changes and have been sufficiently collected. The data we use for our time series forecast is collected from Opportunity Insight’s EconomicTracker dataset, which span from early January to late October and could be considered as up to date.

4.1 ARIMA Model To predict these economic factors for the near future, we choose the ARIMA model. ARIMA models or auto-regressive moving-average models with exogenous are a family of auto-regressive time series models used in econometrics and statistics. This model has some similarities to the typical linear regression model. There are four main parameters that need to be identified with an ARMA model. They include . p, q, d, and .∊. The properties of the dataset you’re analyzing will change what parameters should be included in the ARMA model and how you need to estimate these parameters. The ARIMA modeling technique is a rather simple yet powerful tool that looks at observations into the patterns of change along with estimating different possible future states given random variables. The ARIMA combines autoregression and moving average and is often shown to be effective in time series prediction tasks. Previous researches have employed ARIMA for COVID-19 cases forecast [9], and some even found that ARIMA outperformed some deep learning models despite the latter’s increasing popularity in

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industry in recent years [8]. Considering that the economic factors we investigate are directly related to the trend of COVID cases, we believe that ARIMA could also be effective in forecasting the employment rate changes and mobility changes induced by the pandemic [5]. Specifically, we set the value of each variable in January as benchmark and predict the percentage of change compared to their respective benchmark. Our forecast results (red part of the curve in the figures) of the employment rates by income levels and time spent at home are present in the Results.

5 Model Training We predict the economic impact of COVID on cities using a sequential neural network architecture with eight inputs and two hidden layers. The most popular sequential neural network architecture is the DenseNet and Devoted Square Matrix. The sequential neural network architectures effortlessly learn in the order of sequences. This architecture is able to process time channels, discrete temporal flow signals, and time-sensitive tasks. It also has convolutional layers as well as non-linear batch normalization layers. The benefits of sequential neural network architecture are: baseline entices tuning and architecture design with recurrent feedbacks; very good results derived from maximum sentences such actions state machine action; use cases—speech recognition, natural language processing, and demand forecasting.

6 Implementation We implement this model to evaluate and predict the consumer spending and business revenue sectors of the economy. After performing dimensionality reduction and other preliminary data analysis, we picked the following variables as input to the neural network: daily COVID case increase, cumulative COVID cases, average income, employment for low-income workers, employment for medium-income workers, employment for high-income workers, total population, and mobility factor [2]. Dimensionality reduction is an iterative process that basically alters a dataset until it is satisfied. Dimensionality reduction is a great option for all of your predictive modeling needs because, in order to successfully predict the future with these models, you need to first remedy the variance of your data spread. The goal with this process— make predictions more accurately by limiting the response output space. To run through dimensionality reduction systematically: find a method–extract features, find best fit–map features and optimal properties of second method–repeat adjustment workings and rerun model until optimum variance found. Dimensionality Reduction can help create processes that are more efficient for predictive models when trying to predict future events or determine best outcomes

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for particular experiences. By following through these steps such as scaling down from large numbers, it reduces variability and therefore allows items or individual events in question to be answered or predicted more accurately than before. We constructed for each label a model based on the input. We can observe the loss decreasing over the epoch over time. With a revised model, we are able to observe the model’s predictions being consistent as the result of time series analysis.

7 Results As shown, we have the forecasted results of the employment rates by income levels and time spent at home. As discussed previously, we observe that the loss has decreased over the epoch over time. After revising the model, we see that the model’s predictions have become consistent, the result of time series analysis. The results are shown in Figs. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, and 14.

Fig. 1 Charlotte low income

Fig. 2 Charlotte mid-income

COVID-19’s Influence on Buyers and Businesses Fig. 3 Charlotte high income

Fig. 4 Chicago low income

Fig. 5 Chicago mid-income

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Fig. 7 LA, low income

Fig. 8 LA, mid-income

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COVID-19’s Influence on Buyers and Businesses Fig. 9 LA, high income

Fig. 10 Charlotte time spent at workplace

Fig. 11 Chicago time spent at workplace

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Fig. 13 Network architecture

Fig. 14 Loss function over time

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8 Conclusion According to our prediction, in 1 year, Chicago will be most severely affected by COVID-19, Los Angeles follows, while Charlotte is the least affected one. This makes economic sense, as Chicago has the greatest number of small businesses, and many of their lockdowns would lead to high unemployment rate. Moreover, Chicago imposes strict social distancing rules, and social mobility would therefore be impaired as people are forced to stay at homes. Both of these are crucial covariates for predicting consumer spending and business revenue. Los Angeles would also be somewhat affected because it has lots of small businesses, though not as many as Chicago, and social distancing rules. Significant decrease in import and export would also lead to its economic depression. Charlotte would righteously be the least affected one by the similar line of reasoning. In 2 or 5 years, there would be much higher uncertainty in prediction, but we can reasonably believe that cities like Chicago with lots of small businesses and strict social distancing rules would still be the most impacted ones, since compared to other industries or agriculture, small businesses are much harder to recover from an economic trough. Closure of deprecation of many small businesses would be irreversible, and its influence can extend to a much wider span of time.

8.1 Future Work Our model is far from comprehensive, as there are still many possible covariates to consider, such as import, export, or traveling, which would all potentially impose great impact on a city’s economy. We also realize that COVID response policies played a vital role in the dynamics of the pandemic, and future economic trends inevitably will depend on new policies. However, our model still demystifies which cities are potentially most affected. In short, our model and analysis predicts that cities with high number of small businesses and decreased mobility, or away-fromhome rate, are likely to suffer the most. These impacts could last more than 1 year, and perhaps up to 5 years, because the short-term changes of economic factors such as decreased employment rate are related to more long-lasting effects, such as bankruptcy of small businesses and even larger chain stores. According to our findings, cities should promptly take actions to tackle unemployment, such as offering short-term job opportunities (projects similar to President Roosevelt’s New Deal could be potential options), before its economic situation exacerbates even more severely. Moreover, since the amount of time residents spend at workplace has shown significant amount of decrease and is likely to persist for a period of time, cities can also consider innovative ways to offer more remote jobs to boost the economy.

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References 1. Bakaritantri PP et al (2022) The role of innovation for knowledge management to predispose business performance of micro, small, and medium enterprise (MSME) in Semarang city during covid-19 outbreak. Admisi Dan Bisnis 22(3):271–282 2. Cheng D, Yang F, Xiang S, Liu J (2022) Financial time series forecasting with multi-modality graph neural network. Pattern Recognit 121:108218 3. Giunipero LC, Denslow D, Rynarzewska AI (2022) Small business survival and covid-19-an exploratory analysis of carriers. Res Transp Econ 93:101087 4. Jin X-B, Gong W-T, Kong J-L, Bai Y-T, Su T-L (2022) PFVAE: a planar flow-based variational auto-encoder prediction model for time series data. Mathematics 10(4):610 5. Jin X-B, Gong W-T, Kong J-L, Bai Y-T, Su T-L (2022) A variational Bayesian deep network with data self-screening layer for massive time-series data forecasting. Entropy 24(3):335 6. Kryshtanovych S, Prosovych O, Panas Y, Trushkina N, Omelchenko V (2022) Features of the socio-economic development of the countries of the world under the influence of the digital economy and covid-19. Int J Comput Sci Netw Secur 22(1):9–14 7. Le TT, Nguyen VK (2022) Effects of quick response to covid-19 with change in corporate governance principles on SMES’ business continuity: evidence in Vietnam. Corp Gov: Int J Bus Soc (2022) 8. Papastefanopoulos V, Linardatos P, Kotsiantis S (2020) Covid-19: a comparison of time series methods to forecast percentage of active cases per population. Appl Sci 10(11):3880 9. Tandon H, Ranjan P, Chakraborty T, Suhag V (2020) Coronavirus (covid-19): Arima based time-series analysis to forecast near future. arXiv:2004.07859 10. Walker JT, Fontinha R, Haak-Saheem W, Brewster C (2022) The effects of the covid-19 ‘lockdown’ on teaching and engagement in UK business schools. In: Organizational management in post pandemic crisis. Springer, pp 1–28

Exploring Textural Behavior of Novel Coronavirus (SARS–CoV-2) Through UV Microscope Images Amit Kumar Shakya, Ayushman Ramola, and Anurag Vidyarthi

Abstract Newly discovered coronavirus disease (Covid-19) is an infectious disease related to respiratory illness. Here a texture-based investigation of the coronavirus is performed by analyzing Ultra Violet (UV) microscopic images. Considering the seriousness of the issue, we have used the “World Health Organization (WHO)” information and the “Center for Disease Control and Prevention (CDC)” in this article. UV microscopic image processing is performed scientifically with the famous and existing image classification texture-based technique. Here we have used a grey level-based co-occurrence approach, a second-order statistical-based approach to analyze UV images. The texture features associated with the images are quantified in four different directions 0°, 45°, 90° and 135° and eight different distances, i.e., d = 1, 2, 3, 4, 5, 6, 7and8. As a result, we have obtained a changing pattern in the Covid19 infected area compared with the human bodies’ non-infected regions. Finally, we have proposed a methodology to analyze UV images of Covid19 patients through this research. The obtained results assist the medical and scientific community in fighting these global epidemics. Keywords Microscope images · Grey level co-occurrence matrix

1 Introduction The novel Coronavirus is assumed to have originated in Wuhan, China, in midSeptember 2019. The world will know about the virus by the start of 2020 or late 2019. By then, the virus had spread in most of the world’s nations. This is a contagious disease, and people get infected by one another. This virus is deadly for persons A. K. Shakya (B) · A. Ramola Department of Electronics and Communication Engineering, Sant Longowal Institute of Engineering and Technology, Sangrur, Punjab, India e-mail: [email protected] A. Vidyarthi Department of Electronics and Communication Engineering, Graphic Era (Deemed to Be University), Dehradun, Uttarakhand, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Jain et al. (eds.), Cybersecurity and Evolutionary Data Engineering, Lecture Notes in Electrical Engineering 1073, https://doi.org/10.1007/978-981-99-5080-5_31

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already suffering from respiratory disorders, asthma, and other viral and infectious diseases. These peoples are more prone to virus attacks. By the time we wrote this article, this virus had been spread to 219 Nations of the World [1]. It has claimed 6,636,525 lives, 646,257,489 people are affected by this virus, and 624,788,172 people have recovered from the virus attack [2]. Today the novel Coronavirus is continuously changing its format. It has developed several other forms. Like the UK has identified a Covid variant known as B.1.1.7 [3], South African variant is known as B.1.351 [4], Brazilian variant of Covid is called P.1 [5], India has also monitored a new Covid variant which is named as delta. Thus the study of novel Coronavirus is a hot research topic where several techniques, methodologies, and procedures are used these days to identify the behaviour of the Covid virus. This research obtained Covid 19 virus images from the open-source image database. We have performed the texture classification of these images based on “Grey Level Cooccurrence Matrix (GLCM)” parameters. GLCM has been widely used in remote sensing applications for analyzing the texture of the land surface, mountains, etc. [6] [7], but GLCM can also be used in biomedical applications. Some of the notable work done in the field of GLCM is presented in these reviews. Usha et al. [8] used GLCM texture features to identify the possibility of breast cancer detection using digital mammograms. Kshirsagar et al. [9] used GLCM derived texture features to detect brain tumors from the patient body. Gupta et al. [10] used GLCM features and PDFBCT for the brain disease classification of the kernel SVM approach. Tamal [11] performed a phantom study to investigate GLCM features for “Positron Emission Tomography.” He combined GLCM features with artificial intelligence to create a model for diagnosing Positron Emission Tomography. Link et al. [12] used GLCM features for the Cataract detection using illumination patterns. GLCM is used in several more applications like feature extraction for lung cancer detection using CT images [13], radionics prediction for end-to-end modelling [14], abnormal blood cell detection [15], etc. Thus GLCM can also be used effectively for biomedical applications. GLCM has advantages like it can provide information about spatial behaviour and its relationship with the spatial organization of the image pixels. Thus in this work, we have applied the GLCM technique to quantify Covid infection in human beings.

2 Background Information About the Novel Coronavirus Several precautions need to be followed to avoid the spreading of the coronavirus. This disease spreads mainly through respiratory droplets when a Coronaviruspositive patient speaks, coughs, and sneezes. This disease also applies when a person touches the contaminated surface, followed by touching the mouth, eyes, and nose of himself or others [16]. Besides, several myths must be eradicated from society to strengthen the fight against coronavirus. Some of the myths about the spreading of coronavirus are as follows.

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• Coronavirus cannot spread on a hot and sunny day. Sunlight and heat may have some adverse effects on the virus, but there is no official confirmation of this theory. Many countries having warm weather have a large number of Coronavirus-positive patients. • Symptoms like dry cough, tiredness, and fever are considered most common in Coronavirus patients. Some patients may develop cough, fatigue, and illness to a new form of pneumonia. Thus the only way to get confirmation about the Coronavirus condition is the Coronavirus lab test. • This assumption is not correct that alcohol consumption is the solution to getting rid of the coronavirus. It only creates health-related problems. • Cold weather has no effects on the coronavirus. A bath in hot water that can kill the virus is also a myth. • Mosquito bites can transmit coronavirus is also a myth. • Getting a fever is the first stage of coronavirus. Thermal scanners only provide information about body temperature, which can be higher for patients suffering from fever. Still, at the same time, fever is not the confirmation of coronavirus, as it takes 2–10 days to convert to the coronavirus. • Coronavirus affects all age groups; there is no evidence that this disease most affects a particular age group. Yet, it has been observed that people with having pre-medical history are more vulnerable to this virus.

3 Information About Texture Parameters of the “Grey Level Co-Occurrence Matrix” Haralick et al. invented GLCM [17]. He computed fourteen different features that are used to calculate the texture of the surface. At the time of the invention of GLCM, no particular category was assigned for the texture features. Later another scientist. Gotlieb et al. [18], classified these fourteen features into four different parameters. Figure 1 presents a detailed classification of the texture features performed by Gotlieb in various categories. This investigation deal with considering texturing visual and energy features. According to other sources, energy is also included in the texture visible feature group. Figure 2 represents the GLCM creation from an input image and its normalization. The following steps must be followed to develop a GLCM from an input image. a. An input test data is having a dimension of 5 × 5 is represented in Fig. 2a. b. The input image contains 25 pixels, and these pixels have assigned a value known as “Digital Number (DN)” or pixel values represented by Fig. 2b. c. The frequency of one digital number followed by another immediately to the right is represented in Fig. 2c. The frequency of “2 followed by 2” and “4 followed by 5” is 2. Thus in the GLCM image, the frequency has been assigned value

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Fig. 1 Classification of the GLCM parameters

Fig. 2 Procedure for the creation of the GLCM

“2,” represented by Fig. 2c. The same procedure has been adopted for other such combinations and has been awarded the calculated frequency value. d. If there is no such combination of the numbers immediately followed to the right, they have been assigned value zero, e.g., “1 followed by 2” have no occurrence possibility; thus, in the GLCM, they have been given value “0”.

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e. Finally, the normalization of the GLCM image is created, represented by Fig. 2d. Texture visual features obtained from the GLCM are expressed by Equation (1)– (4). Their mathematical notations and the operational ranges are defined individually. Let the input image I (s, t) be having s representing rows number whereas t represents column number. Then all the pixels contained in the image are expressed by (s × t). The pixel around which GLCM is to be calculated is P(u, v). The number of unique grey levels is denoted by Dg. Then the three visible texture features and energy features are presented by Equations (1)–(4). A. Contrast ⎧ ⎫ Dg −1 Dg Dg ⎨∑ ⎬ ∑ ∑ f m1 = z2 × P(u, v) (1) ⎩ ⎭ n=0

m=1 n=1

Here the difference generated between the pixel values |u − v|. If this difference is zero, the image is assumed to have a constant appearance. B. Correlation f m2 =

∑ ∑ (m, n)P(q, r ) − μm μn m

σm σn

n

(2)

Here σm , σn and μm , μn and are the “standard deviation” and “mean” of the row and column, respectively and its value lies in the range of [−1, 1]. C. Homogeneity f3 =

∑∑ m

n

1 P(u, v) 1 + (m − n)2

(3)

It is expressed with the assistance of the formula mentioned above. The range of homogeneity is expressed in between [0, 1]. D. Energy f4 =

∑∑ m

(P(u, v))2

(4)

n

This feature is defined as the squared of the image pixels. The energy feature lies in between [0, 1].

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4 Related Work Texture classification is an essential approach through which the surfaces’ statistical properties are analyzed [19] [20]. Texture classification approaches play an important role in remote sensing [21], image processing [22], medical imaging [23], biomedical imaging [24], etc. There are different approaches to texture classification like objectbased approach [25], classified object-based policy [26], geographical information system (GIS) [27], artificial neural network (ANN) [28], deep learning techniques [29], GLCM based texture analysis approach [30, 31], etc. Among all these techniques, the GCLM-based texture classification technique is considered an effective method as it provides information about both the statistical and spectral components of an image [32, 20]. Some of the notable work done in texture classification through GLCM are discussed in these reviews. Oghuz et al. [33] developed a texture-based model using the GLCM approach to detect skin-related issues like “skin disorder, lesion recognization, nudity detection.” Zhang et al. [34] used three statistical texture parameters to identify the rock texture pattern. They used “relative entropy, inverse difference moment, and entropy” for trace detection from the sample rock. Das et al. [35] developed a texture analysis-based technique to analyze liver cancer in the DICOM images. Junior et al. [36] proposed a texture analysis-based database named “Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI)” for “Computer-aided lung cancer diagnosis.” Chi et al. [37] used a GLCM-based texture analysis approach to differentiate between Grade 1 to Grade 3 mammography tumors of the Breast. Srivastava et al. [38] used a GLCM based texture quanitification approach to identify a pattern in the DTD database images. Malkov [39] proposed an investigation of the mammographic texture features associated with breast cancer complications. Thus, the authors obtained information about women’s breast density through the texture feature analysis. Therefore new information is gathered from monitoring the condition of the breasts. Mahagaonkar et al. [40] used texture analysis based on the colour GLCM approach and CS LBP features to detect skin-related melanoma disorder. Arabi et al. [41] used GLCM based texture analysis approach to identify and analyze skin-related conditions like “allergic skin disorders, viral skin disease, bacterial skin diseases, and fungal skin diseases.” Shaharuddin et al. [42] used texture analysis based on the GLCM approach to detect kidney abnormalities by analyzing Ultrasound images of kidneys. Their study also contains texture features investigation and visual change in the image parameters obtained through intensity histogram. Tan et al. [43] used a GLCM approach to monitor the changes at the early stages of preimplantation embryos. The authors have obtained an additional edge over the conventional methods to identify “staining intensity for metabolic markers.” Horie [44] used texture feature information derived from GLCM to study various kidney-related problems. Through the texture analysis, the approach authors could distinguish between texture patterns, i.e., “rough or bumpy.” They also announced GLCM as a “standardized image-acquisition protocol,” whose analysis needs to be performed earlier to obtain new image features. Kanagaraj et al. [45] used a GLCM-based approach to detect Pulmonary Tumors developing in the human body.

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They used visual texture features to identify the Tumor for the lung nodule. Tan [46] proposed a 3-dimensional GLCM-based texture feature model with the assistance of CNN. They used their proposed model to differentiate the “poly-classification versus CT colonography” model.

5 Experimental Results In this research work, we are performing a texture-based investigation of the coronavirus. The UV microscope images are analysed based on the texture-based GLCM features. Sample images of coronavirus obtained from various online outlets are presented in Fig. 3. This experiment has taken two different UV images of the coronavirus presented in Fig. 4. The texture of all the images is quantified based on the GLCM parameter in four different directions (orientation angles) and eight different distances. Later the average of the features is performed to make the GLCM direction independent through texture quantification, how the texture of the images varies in different directions and orientations. It is also assumed that for a Covid 1 image, the behaviour of the texture features is other, and for the Covid 2 image, texture features behave differently. The objective of a texture-based analysis of an electron microscope (EM) image of a Novel Coronavirus is to detect the statistical changes developed in the human tissues due to the spread of this virus. Texture visual features contain three different features contrast, correlation, and homogeneity. So these features must have specific

Fig. 3 Sample UV and electron microscope images of the virus

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Fig. 4 Scanning electron microscope image of Novel Coronavirus

(a) Covid 1

(b) Covid 2

behaviour in the coronavirus-infected and non-infected areas. The values of the statistical texture feature for Fig. 4a, i.e., Covid 1 and Fig. 4b, i.e., Covid 2, are represented in Tables 1 and 2, respectively. The grey-level representation of the Coronavirus Covid 1 and Covid 2 is presented in Figs. 5e and 6e, respectively. Where Eng* = energy, Cont* = contrast, Corre* = correlation and Homo* = homogeniety. From the visual representation of the Covid virus’s grey-level images, the Covidinfected areas’ heat signatures can be easily identified. In Fig. 5e, white squares highlight the regions infected with the Covid 1 image. Similarly, Fig. 6e represents the change in the features for the Covid 2 image and the grey representation of the image. The black squares highlighted the area most affected by the coronavirus. The Table 1 Quantification of the statistical parameter of Covid 1 image S.No Deg 1

0°

Parameters D = 1 D = 2 D = 3 D = 4 D = 5 D = 6 D = 7 D = 8 Cont*

0.2009 0.4577 0.7050 0.9223 1.1229 1.3129 1.4922 1.6613

2

Corre*

0.9673 0.9257 0.8856 0.8504 0.8181 0.7875 0.7586 0.7315

3

Eng*

0.2786 0.2582 0.2455 0.2357 0.2272 0.2195 0.2124 0.2059

Homo*

0.9194 0.8723 0.8443 0.8244 0.8079 0.7936 0.7804 0.7683

Cont*

0.3164 0.6884 1.0111 1.3038 1.5793 1.8391 2.0799 2.3027

4 5

45°

6

Corre*

0.9486 0.8884 0.8363 0.7893 0.7452 0.7037 0.6655 0.6302

7

Eng*

0.2674 0.2451 0.2302 0.2176 0.2061 0.1954 0.1858 0.1772

Homo*

0.8941 0.8458 0.8166 0.7941 0.7734 0.7544 0.7374 0.7219

8 9

90◦

Cont*

0.2177 0.5034 0.7778 1.0233 1.2489 1.4606 1.6607 1.8501

10

Corre*

0.9646 0.9182 0.8738 0.8340 0.7976 0.7635 0.7313 0.7009

11

Eng*

0.2766 0.2549 0.2405 0.2291 0.2194 0.2103 0.2020 0.1942

Homo*

0.9151 0.8671 0.8380 0.8165 0.7991 0.7835 0.7692 0.7554

12 13 14

135° Cont* Corre*

0.3251 0.7113 1.0425 1.3387 1.6130 1.8660 2.1020 2.3216 0.9472 0.8847 0.8312 0.7836 0.7397 0.6994 0.6619 0.6272

15

Eng*

0.2666 0.2440 0.2284 0.2155 0.2043 0.1941 0.1848 0.1764

16

Homo*

0.8931 0.8443 0.8146 0.7916 0.7718 0.7537 0.7378 0.7234

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Table 2 Quantification of the statistical parameter of Covid 2 image S.No Degree Features D = 1 D = 2 D = 3 D = 4 D = 5 D = 6 D = 7 D = 8 Cont*

0.1198 0.2268 0.3206 0.4023 0.4762 0.5444 0.6090 0.6710

2

Corre*

0.9632 0.9304 0.9016 0.8766 0.8540 0.8331 0.8134 0.7945

3

Eng*

0.2251 0.1958 0.1792 0.1693 0.1629 0.1581 0.1542 0.1505

4

Homo*

0.9416 0.9010 0.8747 0.8563 0.8436 0.8330 0.8238 0.8152

1

0°

Cont*

0.1696 0.3061 0.4128 0.5052 0.5906 0.6730 0.7527 0.8281

6

Corre*

0.9480 0.9062 0.8736 0.8455 0.8196 0.7947 0.7706 0.7479

7

Eng*

0.2096 0.1810 0.1685 0.1615 0.1562 0.1517 0.1472 0.1429

5

45°

8

Homo*

0.9211 0.8785 0.8557 0.8405 0.8283 0.8174 0.8067 0.7967

Cont*

0.1243 0.2342 0.3285 0.4097 0.4830 0.5513 0.6167 0.6807

10

Corre*

0.9618 0.9282 0.8993 0.8745 0.8522 0.8314 0.8116 0.7922

11

Eng*

0.2236 0.1944 0.1784 0.1692 0.1631 0.1586 0.1548 0.1514

12

Homo*

0.9395 0.8986 0.8729 0.8558 0.8430 0.8325 0.8235 0.8150

Cont*

0.1710 0.3154 0.4359 0.5430 0.6411 0.7323 0.8169 0.8963

14

Corre*

0.9475 0.9033 0.8666 0.8340 0.8042 0.7766 0.7510 0.7271

15

Eng*

0.2092 0.1797 0.1656 0.1577 0.1520 0.1470 0.1424 0.1379

16

Homo*

0.9201 0.8753 0.8494 0.8320 0.8184 0.8062 0.7950 0.7841

9

13

0°

135°

Covid 1 image average of image texture features is plotted to observe the change in the texture features of GLCM. The plotting of the statistical features suggests that texture feature contrast increases with an increase in the distance of the “pixel of interest (POI)” from its neighbouring pixels. Similarly, the texture feature energy, correlation, and homogeneity show a decreasing pattern with the increase in the distance of POI from its adjacent pixels. The average values of the texture features are performed to make GLCM independent of the direction. The mean values of the statistical features are represented in Fig. 7. The plotting of the statistical characteristics for the Covid 2 image is shown in Fig. 6, which follows approximately the same pattern as the Covid 1 image. The value of the texture feature contrast increases with an increase in the distance of POI from its neighbouring pixels, whereas correlation, energy, and homogeneity show opposite behaviour. Finally, the average of the feature value is obtained to make the GLCM direction independent, shown in Fig. 8.

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Range of Contrast

1.5

Range of Correlation

0 Degree 45 Degree 90 Degree 135 Degree

2

1

Variation in the Texture feature (Correlation) 1 0 Degree 0.95 45 Degree 90 Degree 0.9 135 Degree 0.85 0.8 0.75 0.7

0.5

0.65

(a)

(b)

0 1

2 3 4 5 6 Neighbouring pixel distance

7

0

Variation in the Texture Feature (Energy) 0 Degree 45 Degree 90 Degree 135 Degree

0.25

Range of Homogeniety

Range of Energy

0.3

0.2

2 4 6 Neighbouring pixel distance

8

Variation in the Texture Feature (Homogeniety) 0.95 0 Degree 45 Degree 0.9 90 Degree 135 Degree 0.85

0.8

0.75 0.15 0

(d)

(c) 2 4 6 Neighbouring pixel distance

8

0.7 0

2 4 6 Neighbouring pixel distance

450 400

Dimension (450)

350 300 250 200 150 100 50 100

200

300

400

500

600

Dimension (650) Fig. 5 Variation in the statistical features and grey level representation of the Covid 1 image

8

Exploring Textural Behavior of Novel Coronavirus (SARS–CoV-2) … Variation in the Texture Feature (Contrast)

365

Variaition in the Texture Feature (Correlation)

0.9

Range of Contrast

0.7

1

0 Degree 45 Degree 90 Degree 135 Degree

0.6 0.5 0.4

0.9 0.85 0.8

0.3 0.2

0.75

(a)

0.1 0

2 4 6 Neighbouring pixel distance

8

Variation in the Texture Feature (Energy)

Range of Homogeniety

0 Degree 45 Degree 90 Degree 135 Degree

0.22

(b) 1

0.24

Range of Energy

0 Degree 45 Degree 90 Degree 135 Degree

0.95 Range of Contrast

0.8

0.2 0.18 0.16

2

3 4 5 6 Neighbouring pixel distance

7

Variation in the Texture Feature (Homogeniety) 0.95 0 Degree 45 Degree 90 Degree 0.9 135 Degree

0.85

0.8

0.14

(c) 0.12 0

(d) 2 4 6 Neighbouring pixel distance

8

0.75 0

2 4 6 Neighbouring pixel distance

Dimension (450)

100 200 300 400 500 100

200

300

400

500

600

Dimension (550) Fig. 6 Variation in the statistical features and grey level representation of the Covid 2 image

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GLCM VISUAL FEATURES

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Variation in the Texture features for COVID 1 Homogeniety Energy Correlation Contrast 0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

RANGE OF TEXTURE FEATURES 135 Degree

90 Degree

45 Degree

0 Degree

GLCM VISUAL FEATURES

Fig. 7 Average value of the texture feature for Covid 1 image

Variation in the Texture features for COVID 2 Homogeniety Energy Correlation Contrast 0

0.2 0.4 0.6 RANGE OF TEXTURE FEATURES

135 Degree

90 Degree

45 Degree

0.8

1

0 Degree

Fig. 8 Average value of the texture feature for Covid 2 image

6 Conclusion In this research work, we have performed a texture-based investigation of the coronavirus. Here we have used a GLCM based approach to detect changes in the texture features of the coronavirus. GLCM is a second-order statistical-based approach, which is helpful as it provides information about the image’s statistical and spectral characteristics. The texture features of the images are quantified in four different directions 0°, 45°, 90° and 135° and eight different distances, i.e., d = 1, 2, 3, 4, 5, 6, 7and8. The GLCM features’ average is plotted to observe the change developed in the image’s statistical characteristics. Finally, comparing the overall standard of visual texture features is performed to identify the pattern established in the feature values for the coronavirus-infected and non-infected areas. Thus texture analysis of the Coronavirus virus can also provide vital information regarding the physical appearance and the changes in the virus with the change in time. Therefore different methodologies, theories, and solutions can be obtained by investigating

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the UV images of the coronavirus. This also presents the ability of image processing techniques to identify and detect irregularities through analysing an image’s spectral and spatial characteristics.

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