Subhabrata Barman is an assistant professor in the Department of Computer Science and Engineering, Haldia Institute of Technology, West Bengal, India, with over 19 years of teaching and research experience. He has edited a number of internationally published books and journals. Additionally, he is a professional member of the Computer Society of India, the Institute for Electrical and Electronics Engineers, the International Association of Computer Science and Information Technology, and the International Association of Engineers. His research interests include wireless networks, computational intelligence, remote sensing and geoinformatics, precision agriculture, and parallel and grid computing. Santanu Koley, PhD, is a professor in the Computer Science and Engineering Department at Haldia Institute of Technology, West Bengal, India, with more than 19 years of teaching experience and more than eighteen years of research experience. He has published over 50 research publications in numerous national and international journals, conferences, books, and book chapters. His main areas of research include machine learning, cloud computing, digital image processing, and artificial intelligence. Subhankar Joardar, PhD, is a professor and head of the Department of Computer Science and Engineering, Haldia Institute of Technology, India. He has published over 20 technical papers in referred journals and conferences. Additionally, he has served as an organizing chair and program committee member for several international conferences and is a member of the Computer Society of India. His current research interests include swarm intelligence, routing in mobile ad hoc networks, and machine learning.

• Preface xxi1 Yet Another Move Towards Securing Video Using Sudoku-Fernet 1Sunanda Jana, Swarnajit Bhattacharya, Mrinmoy Sen, Abhinandan Khan, Arnab Kumar Maji and Rajat Kumar Pal1.1 Introduction 11.2 Literature Survey 61.3 Proposed Methodology 81.4 Result Analysis 121.5 Computational Complexity 141.6 Conclusions 15References 152 Watermarking: Characteristics, Methods, and Evaluation 17Soumitra Roy and Bappaditya Chakraborty2.1 Introduction 182.2 Watermark Definition 202.3 Properties of Watermarking 222.4 Categorization of Watermarking 252.5 Attacks on Watermarking 292.6 Chapter Summary 32References 323 A Comprehensive Study on Deep Learning and Artificial Intelligence for Malware Analysis 39Tukkappa Gundoor and Sridevi3.1 Introduction 403.2 The Evolving Landscape of Malware Threats 403.3 The Role of Deep Learning and AI in Enhancing Cybersecurity 423.4 Deep Learning Models for Malware Analysis 453.5 AI Techniques in Malware Analysis 503.6 Challenges and Limitations in Malware Family Classification 513.7 Future Directions 54References 554 Transmit Texts Covertly Using Trigonometric Functions and Pythagorean Theorem 61Nagadevi Bala Nagaram, R. Narmada Devi and S. Karpagam4.1 Introduction 624.2 Mainstream Definition 694.3 Description of the Work 734.4 Algorithm for Decryption 754.5 Conclusion 76References 765 Exploring the Synergy of Cybersecurity and Blockchain: Strengthening Digital Defenses 79Mohan Kumar Dehury, Bhabendu Kumar Mohanta, Manorama Patnaik, Biresh Kumar and Purushottam Kumar5.1 Introduction 805.2 Blockchain Infrastructure 815.3 Literature Review 865.4 Cybersecurity Fundamentals 875.5 Synergies Between Blockchain and Cybersecurity 935.6 Applications of Blockchain and Cybersecurity 945.7 Challenges and Considerations 955.8 Future Directions and Innovations 975.9 Conclusion 98References 996 Protecting in the Digital Age: A Comprehensive Examination of Cybersecurity and Legal Implications 105Nazeer Shaik, B. Hari Chandana, P. Chitralingappa and C. Sasikala6.1 Introduction 1066.2 First-Order Heading 1076.3 Data Protection and Privacy Laws 1116.4 Intellectual Property Rights in Cyberspace 1166.5 Cybersecurity Regulations and Compliance 1176.6 Cybersecurity Incident Response and Reporting 1176.7 International Laws and Jurisdiction in Cybersecurity 1186.8 Liability and Responsibility in Cybersecurity 1226.9 Government Surveillance and Cybersecurity 1236.10 Cybersecurity and Employment Law 1246.11 Cybersecurity and E-Commerce 1266.12 Emerging Legal Issues in Cybersecurity 1276.13 Result 1316.14 Conclusion 132References 1337 A Novel Non-Orthogonal Multiple Access Scheme for Next Generation Millimeter-Wave 5G Communications 137Udayakumar Easwaran and Krishnaveni Vellingiri7.1 Introduction 1387.2 Related Works 1417.3 MIMO–NOMA Systems 1437.4 Phase Noise 1507.5 Results and Discussion 1537.6 Conclusion 156References 1568 Generation of Key Predistribution Scheme Applying Quasi-Symmetric Designs and Bent Functions in the Wireless Sensor Network 159Debashis Ghosh8.1 Introduction 1608.2 Background 1638.3 Our Proposed Scheme 1788.4 Conclusion 182References 1839 Enhanced Security Measures Within the ITS Infrastructure Through the Application of Machine Learning Algorithms for Anomaly Detection 187Shiplu Das, Soumi De, Ananya Ghosh, Sovraj Dey and Tania Bhattacharjee9.1 Introduction 1889.2 Literature Review 1909.3 Proposed Work 1979.4 Methodology Analysis and Discussion 1999.5 Conclusion 202References 20310 The Impact of Distributed Ledger in IoT: A Comprehensive Overview 205Rick Hore, Rishav Dan, Abhijit Sarkar and Sabyasachi Samanta10.1 Introduction 20610.2 Related Work 21210.3 The Potential of DTL in IoT Application 21510.4 Current Use Cases of IoT and DLT 21810.5 Opportunities and Challenges of Integrating DLT with IoT 22010.6 The Future of DLT in IoT Ecosystems 22210.7 Conclusion 224References 22511 A Cryptographic Technique Using Chemicals and Graphs 229Kala Raja Mohan, Nagadevi Bala Nagaram, R. Narmada Devi, Regan Murugesan and Subashini Chandrasekar11.1 Introduction 23011.2 Standard Definitions 23111.3 Periodic Table 23211.4 Coding Table with Chemical Elements 23211.5 Encryption Algorithm 23311.6 Encryption Process—Example 23311.7 Algorithm for Decryption 23511.8 Decryption Process-Example 23511.9 Conclusion 236References 23612 Federated Learning: A Secure Distributed Machine Learning Approach for IoT Technology 239Rituparna Saha and Amit Biswas12.1 Introduction 24012.2 Categorization of FL 24312.3 Data Availability 24412.4 Federated Learning Training Approaches 24412.5 Key Research Directions Related to FL 24512.6 Application Areas of FL 24612.7 Conclusion 250References 25013 Security Analysis for Mobile Crowdsensing Scheme by Predicting Vehicle Mobility Using Deep Learning Algorithm 257Monojit Manna, Arpan Adhikary and Sima Das13.1 Introduction 25813.2 Related Work 25913.3 System Model 26113.4 Model of Threat in Mobile Crowdsensing 26213.5 DL-Based Authentication 26413.6 Dl-Based Privacy Protection 26513.7 False Sensing Countermeasures Based on dl 26713.8 Dl-Based Detection of Intrusion 26713.9 The DLMV Approach’s Design 26913.10 Experimental Result 27213.11 Conclusion 273References 27414 A Study on Protection of Multimedia System Contents Using a Biometric-Based Encryption Technique 277Pinaki Pratim Acharjya, Santanu Koley, Subhabrata Barman, Subhankar Joardar and Jayeeta Majumder14.1 Introduction 27714.2 Literature Survey 28114.3 Multimedia Content Protection 28314.4 Encryption/Decryption in Biometrics 28414.5 The Process 28714.6 Experimental Results 28914.7 Conclusion 290References 29015 Deep Learning Algorithms for Detecting Network Attacks—An Overview 293R. Mythili and A.S. Aneetha15.1 Introduction 29415.2 Technologies of Network Security 29615.3 Network Attacks 29915.4 Deep Learning Approaches 30215.5 Models of IDS 31115.6 IDS Datasets 31215.7 Result Analysis 31415.8 Evaluation Metrics 31515.9 Conclusion 320References 32116 Deep Learning Techniques for Detection of Fake News in Social Media with Huge Data 325Namratha M., Rajeshwari B. S. and Jyothi S. Nayak16.1 Introduction 32616.2 Related Work 32916.3 Proposed Work 33216.4 Results and Discussion 33716.5 Conclusion 34216.6 Future Work 343References 34417 A Secure IoT-Based Heart Rate Monitoring and Analyzing System 347Soumya Roy, Rajib Manna, Sabyasachi Samanta, Moumita Sahoo and Somak Karan17.1 Introduction 34817.2 Literature Review 35317.3 Methodology 35517.4 Result Analysis 36917.5 Conclusion 376References 37718 A Secure IoT-Based Approach for Smart Irrigation System Using an Arduino Uno Microcontroller 379Nitesh Kumar, Soumen Ghosh, Sabyasachi Samanta, Abhijit Sarkar and Priyatosh Jana18.1 Introduction 38018.2 Literature Review 38418.3 Methodology 38818.4 Result Analysis 39518.5 Conclusion 39618.6 Future Aspect 396Acknowledgments 397References 39719 Machine Learning Applications, Challenges, and Securities for Remote Healthcare: A Systematic Review 401Arpan Adhikary, Sima Das, Asit Kumar Nayek, Monojit Manna and Rabindranath Sahu19.1 Introduction 40219.2 Definition of Remote Monitoring of Patients 40319.3 Difference Between the Terminologies “Remote Health Care” and “Remote Healthcare” 40419.4 Components of the Remote Healthcare System 40419.5 Benefits of Remote Healthcare 40519.6 Challenges in the Remote Healthcare System 40619.7 Application Areas of Machine Learning in the Remote Healthcare System 40619.8 The Advantage of Remote Monitoring System 40819.9 Important Features and Factors of the Remote Monitoring System 40919.10 Sensors Needed for the Wireless Body Area Network (wban) 41119.12 Machine Learning Solution for Remote Monitoring 41319.13 Internet of Things Solution for Remote Monitoring 41419.14 Security Solution for the Remote Monitoring 41619.15 Conclusion 416References 41720 Enhancing Video Steganography Security for Cross-Platform Applications: A Focus on High-Definition Formats and Streaming Environments 421Santanu Koley and Ankur Kumar20.1 Introduction 42220.2 Video Steganography 42220.3 The Compressed Domain 42220.4 Coding Concepts 42320.5 Temporal Model 42420.6 Macroblocks Motion Estimation 42520.7 Steganalysis 42520.8 Cryptography 42820.9 Steganographic Encoder 43520.10 Conclusion 43720.11 Future Work 438References 438Index 441

Subhabrata Barman is an assistant professor in the Department of Computer Science and Engineering, Haldia Institute of Technology, West Bengal, India, with over 19 years of teaching and research experience. He has edited a number of internationally published books and journals. Additionally, he is a professional member of the Computer Society of India, the Institute for Electrical and Electronics Engineers, the International Association of Computer Science and Information Technology, and the International Association of Engineers. His research interests include wireless networks, computational intelligence, remote sensing and geoinformatics, precision agriculture, and parallel and grid computing. Santanu Koley, PhD, is a professor in the Computer Science and Engineering Department at Haldia Institute of Technology, West Bengal, India, with more than 19 years of teaching experience and more than eighteen years of research experience. He has published over 50 research publications in numerous national and international journals, conferences, books, and book chapters. His main areas of research include machine learning, cloud computing, digital image processing, and artificial intelligence. Subhankar Joardar, PhD, is a professor and head of the Department of Computer Science and Engineering, Haldia Institute of Technology, India. He has published over 20 technical papers in referred journals and conferences. Additionally, he has served as an organizing chair and program committee member for several international conferences and is a member of the Computer Society of India. His current research interests include swarm intelligence, routing in mobile ad hoc networks, and machine learning.

• Preface xxi1 Yet Another Move Towards Securing Video Using Sudoku-Fernet 1Sunanda Jana, Swarnajit Bhattacharya, Mrinmoy Sen, Abhinandan Khan, Arnab Kumar Maji and Rajat Kumar Pal1.1 Introduction 11.2 Literature Survey 61.3 Proposed Methodology 81.4 Result Analysis 121.5 Computational Complexity 141.6 Conclusions 15References 152 Watermarking: Characteristics, Methods, and Evaluation 17Soumitra Roy and Bappaditya Chakraborty2.1 Introduction 182.2 Watermark Definition 202.3 Properties of Watermarking 222.4 Categorization of Watermarking 252.5 Attacks on Watermarking 292.6 Chapter Summary 32References 323 A Comprehensive Study on Deep Learning and Artificial Intelligence for Malware Analysis 39Tukkappa Gundoor and Sridevi3.1 Introduction 403.2 The Evolving Landscape of Malware Threats 403.3 The Role of Deep Learning and AI in Enhancing Cybersecurity 423.4 Deep Learning Models for Malware Analysis 453.5 AI Techniques in Malware Analysis 503.6 Challenges and Limitations in Malware Family Classification 513.7 Future Directions 54References 554 Transmit Texts Covertly Using Trigonometric Functions and Pythagorean Theorem 61Nagadevi Bala Nagaram, R. Narmada Devi and S. Karpagam4.1 Introduction 624.2 Mainstream Definition 694.3 Description of the Work 734.4 Algorithm for Decryption 754.5 Conclusion 76References 765 Exploring the Synergy of Cybersecurity and Blockchain: Strengthening Digital Defenses 79Mohan Kumar Dehury, Bhabendu Kumar Mohanta, Manorama Patnaik, Biresh Kumar and Purushottam Kumar5.1 Introduction 805.2 Blockchain Infrastructure 815.3 Literature Review 865.4 Cybersecurity Fundamentals 875.5 Synergies Between Blockchain and Cybersecurity 935.6 Applications of Blockchain and Cybersecurity 945.7 Challenges and Considerations 955.8 Future Directions and Innovations 975.9 Conclusion 98References 996 Protecting in the Digital Age: A Comprehensive Examination of Cybersecurity and Legal Implications 105Nazeer Shaik, B. Hari Chandana, P. Chitralingappa and C. Sasikala6.1 Introduction 1066.2 First-Order Heading 1076.3 Data Protection and Privacy Laws 1116.4 Intellectual Property Rights in Cyberspace 1166.5 Cybersecurity Regulations and Compliance 1176.6 Cybersecurity Incident Response and Reporting 1176.7 International Laws and Jurisdiction in Cybersecurity 1186.8 Liability and Responsibility in Cybersecurity 1226.9 Government Surveillance and Cybersecurity 1236.10 Cybersecurity and Employment Law 1246.11 Cybersecurity and E-Commerce 1266.12 Emerging Legal Issues in Cybersecurity 1276.13 Result 1316.14 Conclusion 132References 1337 A Novel Non-Orthogonal Multiple Access Scheme for Next Generation Millimeter-Wave 5G Communications 137Udayakumar Easwaran and Krishnaveni Vellingiri7.1 Introduction 1387.2 Related Works 1417.3 MIMO–NOMA Systems 1437.4 Phase Noise 1507.5 Results and Discussion 1537.6 Conclusion 156References 1568 Generation of Key Predistribution Scheme Applying Quasi-Symmetric Designs and Bent Functions in the Wireless Sensor Network 159Debashis Ghosh8.1 Introduction 1608.2 Background 1638.3 Our Proposed Scheme 1788.4 Conclusion 182References 1839 Enhanced Security Measures Within the ITS Infrastructure Through the Application of Machine Learning Algorithms for Anomaly Detection 187Shiplu Das, Soumi De, Ananya Ghosh, Sovraj Dey and Tania Bhattacharjee9.1 Introduction 1889.2 Literature Review 1909.3 Proposed Work 1979.4 Methodology Analysis and Discussion 1999.5 Conclusion 202References 20310 The Impact of Distributed Ledger in IoT: A Comprehensive Overview 205Rick Hore, Rishav Dan, Abhijit Sarkar and Sabyasachi Samanta10.1 Introduction 20610.2 Related Work 21210.3 The Potential of DTL in IoT Application 21510.4 Current Use Cases of IoT and DLT 21810.5 Opportunities and Challenges of Integrating DLT with IoT 22010.6 The Future of DLT in IoT Ecosystems 22210.7 Conclusion 224References 22511 A Cryptographic Technique Using Chemicals and Graphs 229Kala Raja Mohan, Nagadevi Bala Nagaram, R. Narmada Devi, Regan Murugesan and Subashini Chandrasekar11.1 Introduction 23011.2 Standard Definitions 23111.3 Periodic Table 23211.4 Coding Table with Chemical Elements 23211.5 Encryption Algorithm 23311.6 Encryption Process—Example 23311.7 Algorithm for Decryption 23511.8 Decryption Process-Example 23511.9 Conclusion 236References 23612 Federated Learning: A Secure Distributed Machine Learning Approach for IoT Technology 239Rituparna Saha and Amit Biswas12.1 Introduction 24012.2 Categorization of FL 24312.3 Data Availability 24412.4 Federated Learning Training Approaches 24412.5 Key Research Directions Related to FL 24512.6 Application Areas of FL 24612.7 Conclusion 250References 25013 Security Analysis for Mobile Crowdsensing Scheme by Predicting Vehicle Mobility Using Deep Learning Algorithm 257Monojit Manna, Arpan Adhikary and Sima Das13.1 Introduction 25813.2 Related Work 25913.3 System Model 26113.4 Model of Threat in Mobile Crowdsensing 26213.5 DL-Based Authentication 26413.6 Dl-Based Privacy Protection 26513.7 False Sensing Countermeasures Based on dl 26713.8 Dl-Based Detection of Intrusion 26713.9 The DLMV Approach’s Design 26913.10 Experimental Result 27213.11 Conclusion 273References 27414 A Study on Protection of Multimedia System Contents Using a Biometric-Based Encryption Technique 277Pinaki Pratim Acharjya, Santanu Koley, Subhabrata Barman, Subhankar Joardar and Jayeeta Majumder14.1 Introduction 27714.2 Literature Survey 28114.3 Multimedia Content Protection 28314.4 Encryption/Decryption in Biometrics 28414.5 The Process 28714.6 Experimental Results 28914.7 Conclusion 290References 29015 Deep Learning Algorithms for Detecting Network Attacks—An Overview 293R. Mythili and A.S. Aneetha15.1 Introduction 29415.2 Technologies of Network Security 29615.3 Network Attacks 29915.4 Deep Learning Approaches 30215.5 Models of IDS 31115.6 IDS Datasets 31215.7 Result Analysis 31415.8 Evaluation Metrics 31515.9 Conclusion 320References 32116 Deep Learning Techniques for Detection of Fake News in Social Media with Huge Data 325Namratha M., Rajeshwari B. S. and Jyothi S. Nayak16.1 Introduction 32616.2 Related Work 32916.3 Proposed Work 33216.4 Results and Discussion 33716.5 Conclusion 34216.6 Future Work 343References 34417 A Secure IoT-Based Heart Rate Monitoring and Analyzing System 347Soumya Roy, Rajib Manna, Sabyasachi Samanta, Moumita Sahoo and Somak Karan17.1 Introduction 34817.2 Literature Review 35317.3 Methodology 35517.4 Result Analysis 36917.5 Conclusion 376References 37718 A Secure IoT-Based Approach for Smart Irrigation System Using an Arduino Uno Microcontroller 379Nitesh Kumar, Soumen Ghosh, Sabyasachi Samanta, Abhijit Sarkar and Priyatosh Jana18.1 Introduction 38018.2 Literature Review 38418.3 Methodology 38818.4 Result Analysis 39518.5 Conclusion 39618.6 Future Aspect 396Acknowledgments 397References 39719 Machine Learning Applications, Challenges, and Securities for Remote Healthcare: A Systematic Review 401Arpan Adhikary, Sima Das, Asit Kumar Nayek, Monojit Manna and Rabindranath Sahu19.1 Introduction 40219.2 Definition of Remote Monitoring of Patients 40319.3 Difference Between the Terminologies “Remote Health Care” and “Remote Healthcare” 40419.4 Components of the Remote Healthcare System 40419.5 Benefits of Remote Healthcare 40519.6 Challenges in the Remote Healthcare System 40619.7 Application Areas of Machine Learning in the Remote Healthcare System 40619.8 The Advantage of Remote Monitoring System 40819.9 Important Features and Factors of the Remote Monitoring System 40919.10 Sensors Needed for the Wireless Body Area Network (wban) 41119.12 Machine Learning Solution for Remote Monitoring 41319.13 Internet of Things Solution for Remote Monitoring 41419.14 Security Solution for the Remote Monitoring 41619.15 Conclusion 416References 41720 Enhancing Video Steganography Security for Cross-Platform Applications: A Focus on High-Definition Formats and Streaming Environments 421Santanu Koley and Ankur Kumar20.1 Introduction 42220.2 Video Steganography 42220.3 The Compressed Domain 42220.4 Coding Concepts 42320.5 Temporal Model 42420.6 Macroblocks Motion Estimation 42520.7 Steganalysis 42520.8 Cryptography 42820.9 Steganographic Encoder 43520.10 Conclusion 43720.11 Future Work 438References 438Index 441

Subhabrata Barman is an assistant professor in the Department of Computer Science and Engineering, Haldia Institute of Technology, West Bengal, India, with over 19 years of teaching and research experience. He has edited a number of internationally published books and journals. Additionally, he is a professional member of the Computer Society of India, the Institute for Electrical and Electronics Engineers, the International Association of Computer Science and Information Technology, and the International Association of Engineers. His research interests include wireless networks, computational intelligence, remote sensing and geoinformatics, precision agriculture, and parallel and grid computing. Santanu Koley, PhD, is a professor in the Computer Science and Engineering Department at Haldia Institute of Technology, West Bengal, India, with more than 19 years of teaching experience and more than eighteen years of research experience. He has published over 50 research publications in numerous national and international journals, conferences, books, and book chapters. His main areas of research include machine learning, cloud computing, digital image processing, and artificial intelligence. Subhankar Joardar, PhD, is a professor and head of the Department of Computer Science and Engineering, Haldia Institute of Technology, India. He has published over 20 technical papers in referred journals and conferences. Additionally, he has served as an organizing chair and program committee member for several international conferences and is a member of the Computer Society of India. His current research interests include swarm intelligence, routing in mobile ad hoc networks, and machine learning.

• Preface xxi1 Yet Another Move Towards Securing Video Using Sudoku-Fernet 1Sunanda Jana, Swarnajit Bhattacharya, Mrinmoy Sen, Abhinandan Khan, Arnab Kumar Maji and Rajat Kumar Pal1.1 Introduction 11.2 Literature Survey 61.3 Proposed Methodology 81.4 Result Analysis 121.5 Computational Complexity 141.6 Conclusions 15References 152 Watermarking: Characteristics, Methods, and Evaluation 17Soumitra Roy and Bappaditya Chakraborty2.1 Introduction 182.2 Watermark Definition 202.3 Properties of Watermarking 222.4 Categorization of Watermarking 252.5 Attacks on Watermarking 292.6 Chapter Summary 32References 323 A Comprehensive Study on Deep Learning and Artificial Intelligence for Malware Analysis 39Tukkappa Gundoor and Sridevi3.1 Introduction 403.2 The Evolving Landscape of Malware Threats 403.3 The Role of Deep Learning and AI in Enhancing Cybersecurity 423.4 Deep Learning Models for Malware Analysis 453.5 AI Techniques in Malware Analysis 503.6 Challenges and Limitations in Malware Family Classification 513.7 Future Directions 54References 554 Transmit Texts Covertly Using Trigonometric Functions and Pythagorean Theorem 61Nagadevi Bala Nagaram, R. Narmada Devi and S. Karpagam4.1 Introduction 624.2 Mainstream Definition 694.3 Description of the Work 734.4 Algorithm for Decryption 754.5 Conclusion 76References 765 Exploring the Synergy of Cybersecurity and Blockchain: Strengthening Digital Defenses 79Mohan Kumar Dehury, Bhabendu Kumar Mohanta, Manorama Patnaik, Biresh Kumar and Purushottam Kumar5.1 Introduction 805.2 Blockchain Infrastructure 815.3 Literature Review 865.4 Cybersecurity Fundamentals 875.5 Synergies Between Blockchain and Cybersecurity 935.6 Applications of Blockchain and Cybersecurity 945.7 Challenges and Considerations 955.8 Future Directions and Innovations 975.9 Conclusion 98References 996 Protecting in the Digital Age: A Comprehensive Examination of Cybersecurity and Legal Implications 105Nazeer Shaik, B. Hari Chandana, P. Chitralingappa and C. Sasikala6.1 Introduction 1066.2 First-Order Heading 1076.3 Data Protection and Privacy Laws 1116.4 Intellectual Property Rights in Cyberspace 1166.5 Cybersecurity Regulations and Compliance 1176.6 Cybersecurity Incident Response and Reporting 1176.7 International Laws and Jurisdiction in Cybersecurity 1186.8 Liability and Responsibility in Cybersecurity 1226.9 Government Surveillance and Cybersecurity 1236.10 Cybersecurity and Employment Law 1246.11 Cybersecurity and E-Commerce 1266.12 Emerging Legal Issues in Cybersecurity 1276.13 Result 1316.14 Conclusion 132References 1337 A Novel Non-Orthogonal Multiple Access Scheme for Next Generation Millimeter-Wave 5G Communications 137Udayakumar Easwaran and Krishnaveni Vellingiri7.1 Introduction 1387.2 Related Works 1417.3 MIMO–NOMA Systems 1437.4 Phase Noise 1507.5 Results and Discussion 1537.6 Conclusion 156References 1568 Generation of Key Predistribution Scheme Applying Quasi-Symmetric Designs and Bent Functions in the Wireless Sensor Network 159Debashis Ghosh8.1 Introduction 1608.2 Background 1638.3 Our Proposed Scheme 1788.4 Conclusion 182References 1839 Enhanced Security Measures Within the ITS Infrastructure Through the Application of Machine Learning Algorithms for Anomaly Detection 187Shiplu Das, Soumi De, Ananya Ghosh, Sovraj Dey and Tania Bhattacharjee9.1 Introduction 1889.2 Literature Review 1909.3 Proposed Work 1979.4 Methodology Analysis and Discussion 1999.5 Conclusion 202References 20310 The Impact of Distributed Ledger in IoT: A Comprehensive Overview 205Rick Hore, Rishav Dan, Abhijit Sarkar and Sabyasachi Samanta10.1 Introduction 20610.2 Related Work 21210.3 The Potential of DTL in IoT Application 21510.4 Current Use Cases of IoT and DLT 21810.5 Opportunities and Challenges of Integrating DLT with IoT 22010.6 The Future of DLT in IoT Ecosystems 22210.7 Conclusion 224References 22511 A Cryptographic Technique Using Chemicals and Graphs 229Kala Raja Mohan, Nagadevi Bala Nagaram, R. Narmada Devi, Regan Murugesan and Subashini Chandrasekar11.1 Introduction 23011.2 Standard Definitions 23111.3 Periodic Table 23211.4 Coding Table with Chemical Elements 23211.5 Encryption Algorithm 23311.6 Encryption Process—Example 23311.7 Algorithm for Decryption 23511.8 Decryption Process-Example 23511.9 Conclusion 236References 23612 Federated Learning: A Secure Distributed Machine Learning Approach for IoT Technology 239Rituparna Saha and Amit Biswas12.1 Introduction 24012.2 Categorization of FL 24312.3 Data Availability 24412.4 Federated Learning Training Approaches 24412.5 Key Research Directions Related to FL 24512.6 Application Areas of FL 24612.7 Conclusion 250References 25013 Security Analysis for Mobile Crowdsensing Scheme by Predicting Vehicle Mobility Using Deep Learning Algorithm 257Monojit Manna, Arpan Adhikary and Sima Das13.1 Introduction 25813.2 Related Work 25913.3 System Model 26113.4 Model of Threat in Mobile Crowdsensing 26213.5 DL-Based Authentication 26413.6 Dl-Based Privacy Protection 26513.7 False Sensing Countermeasures Based on dl 26713.8 Dl-Based Detection of Intrusion 26713.9 The DLMV Approach’s Design 26913.10 Experimental Result 27213.11 Conclusion 273References 27414 A Study on Protection of Multimedia System Contents Using a Biometric-Based Encryption Technique 277Pinaki Pratim Acharjya, Santanu Koley, Subhabrata Barman, Subhankar Joardar and Jayeeta Majumder14.1 Introduction 27714.2 Literature Survey 28114.3 Multimedia Content Protection 28314.4 Encryption/Decryption in Biometrics 28414.5 The Process 28714.6 Experimental Results 28914.7 Conclusion 290References 29015 Deep Learning Algorithms for Detecting Network Attacks—An Overview 293R. Mythili and A.S. Aneetha15.1 Introduction 29415.2 Technologies of Network Security 29615.3 Network Attacks 29915.4 Deep Learning Approaches 30215.5 Models of IDS 31115.6 IDS Datasets 31215.7 Result Analysis 31415.8 Evaluation Metrics 31515.9 Conclusion 320References 32116 Deep Learning Techniques for Detection of Fake News in Social Media with Huge Data 325Namratha M., Rajeshwari B. S. and Jyothi S. Nayak16.1 Introduction 32616.2 Related Work 32916.3 Proposed Work 33216.4 Results and Discussion 33716.5 Conclusion 34216.6 Future Work 343References 34417 A Secure IoT-Based Heart Rate Monitoring and Analyzing System 347Soumya Roy, Rajib Manna, Sabyasachi Samanta, Moumita Sahoo and Somak Karan17.1 Introduction 34817.2 Literature Review 35317.3 Methodology 35517.4 Result Analysis 36917.5 Conclusion 376References 37718 A Secure IoT-Based Approach for Smart Irrigation System Using an Arduino Uno Microcontroller 379Nitesh Kumar, Soumen Ghosh, Sabyasachi Samanta, Abhijit Sarkar and Priyatosh Jana18.1 Introduction 38018.2 Literature Review 38418.3 Methodology 38818.4 Result Analysis 39518.5 Conclusion 39618.6 Future Aspect 396Acknowledgments 397References 39719 Machine Learning Applications, Challenges, and Securities for Remote Healthcare: A Systematic Review 401Arpan Adhikary, Sima Das, Asit Kumar Nayek, Monojit Manna and Rabindranath Sahu19.1 Introduction 40219.2 Definition of Remote Monitoring of Patients 40319.3 Difference Between the Terminologies “Remote Health Care” and “Remote Healthcare” 40419.4 Components of the Remote Healthcare System 40419.5 Benefits of Remote Healthcare 40519.6 Challenges in the Remote Healthcare System 40619.7 Application Areas of Machine Learning in the Remote Healthcare System 40619.8 The Advantage of Remote Monitoring System 40819.9 Important Features and Factors of the Remote Monitoring System 40919.10 Sensors Needed for the Wireless Body Area Network (wban) 41119.12 Machine Learning Solution for Remote Monitoring 41319.13 Internet of Things Solution for Remote Monitoring 41419.14 Security Solution for the Remote Monitoring 41619.15 Conclusion 416References 41720 Enhancing Video Steganography Security for Cross-Platform Applications: A Focus on High-Definition Formats and Streaming Environments 421Santanu Koley and Ankur Kumar20.1 Introduction 42220.2 Video Steganography 42220.3 The Compressed Domain 42220.4 Coding Concepts 42320.5 Temporal Model 42420.6 Macroblocks Motion Estimation 42520.7 Steganalysis 42520.8 Cryptography 42820.9 Steganographic Encoder 43520.10 Conclusion 43720.11 Future Work 438References 438Index 441