K. Ananthajothi, PhD is a Professor in the Department of Computer Science and Engineering at Rajalakshmi Engineering College, Chennai, India. He has published one book, two patents, and several research papers in international journals and conferences. His research focuses on machine learning and deep learning. S. N. Sangeethaa, PhD is a Professor in the Department of Computer Science and Engineering at the Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India. She has published seven books, more than 25 research articles in reputable journals, and more than 50 papers in national and international conferences. Her research interests include artificial intelligence, machine learning, and image processing. D. Divya, PhD is an Assistant Professor in the Department of Computer Science and Engineering at Misrimal Navajee Munoth Jain Engineering College, Chennai, India. She has published several papers in international journals. Her research focuses on data mining and machine learning. S. Balamurugan, PhD is the Director of Albert Einstein Engineering and Research Labs and the Vice-Chairman of the Renewable Energy Society of India. He has published more than 60 books, 300 articles in national and international journals and conferences, and 200 patents. His research interests include artificial intelligence, augmented reality, Internet of Things, big data analytics, cloud computing, and wearable computing. Sheng-Lung Peng, PhD is a Professor and the Director of the Department of Creative Technologies and Product Design at the National Taipei University of Business, Taiwan. He has published more than 100 research papers in addition to his role as a visiting professor and board member for several international universities and academic groups. His research interests include designing and analyzing algorithms for bioinformatics, combinatorics, data mining, and networks.

• Preface xvii1 Enhancing Safety and Security in Autonomous Connected Vehicles: Fusion of Optimal Control With Multi-Armed Bandit Learning 1K.T. Meena Abarna, A. Punitha and S. Sathiya1.1 Background 21.1.1 Problem Statement 41.1.2 Motivation 41.2 Related Works 51.2.1 Contributions 71.2.2 Centralized CRN Scheduling 81.2.3 Multi-Armed Bandit (MAB) 91.2.4 Bandit Learning with Switching Costs 111.3 System Model 121.3.1 Resource Spectrum 121.3.2 CRs’ Spectrum Utilization Schemes 131.3.3 CBS Scheduling 131.3.4 PUs’ Activity 131.4 Outcomes 151.4.1 Scenario I: Fallen Traffic Signs 151.4.2 Scenario II: Traffic Signs Alert by the Road Workers 161.4.3 Scenario III: Back/Rotated Traffic Sign Across the Road 171.4.4 Scenario IV: Hacking of a Stop Sign at a Four-Way Stop Intersection 181.5 Conclusions and Future Enhancement 191.5.1 Conclusions 191.5.2 Future Directions 21References 232 Secure Data Handling in AI and Proactive Response Network: Create a Physical Layer–Proposed Cognitive Cyber-Physical Security 25A. Sivasundari, P. Kumar, S. Vinodhkumar and N. Duraimurugan2.1 Introduction 262.1.1 The Role of AI in Cybersecurity 272.1.2 Usage of CCPS in IoT 272.2 Challenges and Mechanisms 282.2.1 Brief Account of Challenges Faced 282.2.2 Innovative Mechanisms 302.3 Using AI to Support Cognitive Cybersecurity 302.3.1 Cognitive Systems 302.3.2 AI in IoT 302.4 Create a Physical Layer–Proposed CCPS 312.4.1 Create a Physical Layer–Proposed CCPS in Healthcare Application 332.4.1.1 Privacy-Aware Collaboration 332.4.1.2 Cycle Model of CCPS 362.4.1.3 Dynamic Security Knowledge Base 362.4.2 Method for Secure Data Handling 362.5 Road Map of Implementation 382.5.1 AI for CCPS-IoT 382.5.2 AI-Enabled Wireless CCPS-IoT to Provide Security 392.6 Conclusions and Future Enhancement 40Future Directions 41References 433 Intelligent Cognitive Cyber-Physical System–Based Intrusion Detection for AI-Enabled Security in Industry 4.0 45V. Mahavaishnavi, R. Saminathan and G. Ramachandran3.1 Introduction 463.1.1 Cyber-Physical Systems 463.1.2 Intelligent Cyber-Physical Systems (ISPS) 473.1.3 Cognitive Cyber-Physical Systems (CCPS) 483.1.4 IDS in Industry 4.0 Using iCCPS 493.1.5 AI in iCCPS-IDS 493.2 Problem Statement 503.3 Motivation 513.4 Research Gap 523.5 Methodology 533.5.1 Training Dataset 543.5.2 Information for Assessment and Instruction 543.5.3 Model 543.5.4 CPS Determined by Cognition Agents 563.5.5 Useful Implementation of the Actual Device 573.6 Importance and Impact of AI-Based Intrusion Detection in iCCPS in Industry 4.0 593.6.1 Need 593.6.2 Challenges 603.7 Conclusions and Future Directions 60Future Directions 61References 634 Resilient Cognitive Cyber-Physical Systems: Conceptual Frameworks, Models, and Implementation Strategies 65R. Manivannan and M.P. Vaishnnave4.1 Introduction 664.1.1 Problem Statement 704.1.2 Motivation 714.2 Materials and Methods 724.3 CCPS Design Challenges 744.4 Cyber-Physical Systems Principles and Paradigms 774.4.1 CCPS Conceptual Framework 794.4.2 CCPS Modeling 814.4.3 Other Modeling Issues in CCPS 824.5 Conclusions and Future Enhancements 834.5.1 Future Enhancements 83References 855 Cognitive Cyber-Physical Security Challenges, Issues, and Recent Trends Over IoT 87Chinnaraj Govindasamy5.1 Introduction 885.1.1 From IoT to CCPS-IoT 935.1.2 Fundamental Cognitive Tasks 945.2 Motivation and Challenges 945.2.1 Motivation 945.2.2 Challenges 955.3 Security 965.3.1 Physical Layer Attacks 985.3.2 Physical Layer Security 995.3.3 Main Constituents 1005.4 Research Gap 1025.5 An Automatic Security Manager for CCPS Using IoT 1035.5.1 Combatting Erroneous Estimations 1035.5.2 Detection and Classification 1045.6 Conclusions and Future Enhancement 104Future Enhancement 105References 1066 Cognitive Cyber-Physical Security With IoT: A Solution to Smart Healthcare System 109P. Shanmugam, Mohamed Iqbal M. and M. Amanullah6.1 Introduction 1106.1.1 Motivation 1126.1.2 Need and Contribution 1136.1.2.1 Need 1136.1.2.2 Contribution 1146.2 Medical CCPS with IoT 1166.2.1 IoT Device for AI Solution 1186.2.2 Traditional Bio-Modality Spoofing Detection 1196.2.3 MCPS Using AI Device 1196.3 Functional and Behavioral Perspectives 1206.4 Modeling and Verification Methods of MCPS 1236.4.1 MCPS Modeling Based on ICE 1246.4.2 MCPS Modeling Based on Component 1256.5 Artificial Intelligence for Cognitive Cybersecurity 1256.5.1 Privacy-Aware Collaboration 1276.5.2 Cognitive Security Cycle Model 1276.6 Conclusions and Future Direction 1286.6.1 Conclusions 1286.6.2 Future Directions 129References 1307 Cognitive Cyber-Physical Security with IoT and ML: Role of Cybersecurity, Threats, and Benefits to Modern Economies and Industries 133P. Anbalagan, A. Kanthimathinathan and S. Saravanan7.1 Introduction 1347.1.1 Key Contributions 1367.1.2 Problem Statement 1377.1.3 Motivation 1387.2 CCPS Associated with IoT 1397.2.1 Reasons in Favor of Cognitive Analytics 1407.2.2 Analyses of Current Cyber Risk Data 1417.3 Materials and Methods 1437.3.1 Role of Cybersecurity in CCPS with IoT and ml 1437.3.2 ml in Cognitive Cyber-Physical Security with IoT 1447.3.3 Threats to Modern Economies and Industries 1447.3.4 Benefits to Modern Economies and Industries 1477.4 Outcomes 1487.4.1 AI-Enabled Management Technology and Approach Taxonomy 1517.4.2 Essential Self-Adapting System Technologies 1517.4.3 Attack Malware Classifier 1517.5 Conclusions and Future Direction 152Future Directions 152References 1548 A Safety Analysis Framework for Medical Cyber-Physical Systems Using Systems Theory 157K. Ananthajothi, K. Balamurugan, D. Divya and T.P. Latchoumi8.1 Introduction 1588.2 Background 1608.2.1 Cyber-Physical Systems 1608.2.2 Quality-of-Service Issues in CPS 1618.2.3 Medical Cyber-Physical Systems 1618.3 The Systems-Based Safety Analysis Observation for MCPS 1628.3.1 Identification of Critical Requirements in MCPS 1628.3.2 A Systems Theory–Based Method for Safety Analysis in Medical Cyber-Physical Systems 1638.3.3 MCPS in Patient-Controlled Analgesia 1658.4 Improved Wireless Medical Cyber-Physical System (IWMCPS) 1668.4.1 Level: Data Acquisition 1668.4.2 Layer: Data Aggregating 1678.4.3 Level: Storing 1678.4.4 Level: Action 1688.4.5 IWMCPS Architectural Research 1688.4.6 Core of Communications and Sensors 1688.5 Hazard Analysis on PCA-MCPS 1698.5.1 System Safety Constraint 1708.5.2 System Safety Control Structure 1708.5.3 Identify Unsafe Control Actions 1708.5.4 Specifying Causes 1718.6 Conclusions and Future Directions 172Future Directions 172References 1749 Cognitive Cybersecurity and Reinforcement Learning: Enhancing Security in CPS-IoT Enabled Healthcare 177A. Arokiaraj Jovith, M. Sangeetha, D. Saveetha and S. Antelin Vijila9.1 Introduction 1789.2 Methodology 1829.2.1 Device AI Solutions 1829.2.2 Detect the Spoofing of Bio-Modality 1829.2.3 Detect the Spoofing of Bio-Modality Using Machine Learning 1839.3 Challenges and Mechanisms 1839.3.1 Challenges 1839.3.2 Innovative Mechanisms 1859.4 Cognitive Cyber-Physical Systems and Reinforcement Learning 1859.4.1 Model Formulation 1889.4.2 AI in CCPS 1899.4.2.1 Privacy-Aware Collaboration 1929.4.2.2 Cognitive Security Cycle Model 1929.4.2.3 Need 1939.4.2.4 Cross-Sectoral Techniques 1939.4.2.5 Actuation and Data Collection 1949.5 Conclusions and Future Directions 1949.6 Future Directions 195References 19610 Navigating the Digital Landscape: Understanding, Detecting, and Mitigating Cyber Threats in an Evolving Technological Era 199Manikandan J., Hemalatha P., Jayashree K. and Rajeswari P.10.1 The Digital Transformation: Shaping Modern Business Dynamics 20010.2 Impact of COVID-19: Accelerating the Digital Shift 20110.3 Online Safety Concerns: Navigating the Digital Landscape 20210.4 Interplay of Digital Technologies: Vulnerabilities and Threats 20410.4.1 Introduction to Digital Technologies 20410.4.2 Case Studies and Examples 20610.5 Rise of Cyber Assaults as a Service: Automating Criminal Activities 20710.6 Evolving Threat Landscape: Understanding Modern Cyber Attacks 21010.7 Beyond Conventional Security Measures: The Need for Advanced Defense 21110.8 Rise of Cyber Assaults as a Service: Automating Criminal Activities 21310.8.1 Introduction to Cyber Assaults as a Service 21310.8.2 Automation of Criminal Activities 21310.8.3 Impact and Implications 21410.9 Evolving Threat Landscape: Understanding Modern Cyber Attacks 21510.9.1 Types of Modern Cyber Attacks 21510.9.2 Implications for Cybersecurity Defense 21610.10 Beyond Conventional Security Measures: The Need for Advanced Defense 21710.10.1 Challenges with Conventional Security Measures 21710.10.2 The Evolution of Advanced Defense 21810.11 Uncovering Cyber Threats: Patterns, Trends, and Detection Methods 21810.11.1 Patterns of Cyber Threats 21810.12 Addressing Advanced Persistent Threats: Challenges and Solutions 22010.12.1 Introduction to Advanced Persistent Threats (APTs) 22010.12.2 Challenges Posed by APTs 22010.12.3 Solutions for Addressing APTs 221References 22211 Defense Strategies for Cyber-Physical Systems 225Rajendran Thanikachalam, T. Nithya, Balaji Sampathkumar and J. Mangayarkarasi11.1 Introduction 22611.2 Threat Landscape in CPS 22811.3 Advanced Defense Strategies 23111.3.1 Anomaly Detection in CPS 23111.3.2 Secure Communication Protocols 23211.3.3 Machine Learning-Driven Defenses 23511.3.4 Zero Trust Model for CPS 23711.3.5 Resilience Techniques for CPS 24011.3.6 Intensive Training and Awareness 24111.3.7 Conclusion and Future Directions 245References 24512 Cybersecurity in the Era of Artificial Intelligence: Challenges and Innovations 249Ashwini A., H. Sehina and Banu Priya Prathaban12.1 Introduction to Cybersecurity Analysis 25012.2 Need for AI in Cybersecurity 25212.3 Current Cybersecurity Techniques 25312.4 Role of AI in Cybersecurity 25512.5 Challenges in AI Enhanced Cybersecurity 25612.6 Quantum Computing and Post Quantum Computing in Cybersecurity 25712.7 AI Powered Encryption Analysis 25912.8 Adaptive Cybersecurity 26112.9 Overall Analysis of AI in Cybersecurity 26212.10 Privacy Preserving AI and Cybersecurity 26312.11 Future Directions and Research Challenges 26412.12 Conclusion 266References 26613 Safeguarding the Virtual Realm: Assessing Cyber Security Challenges and Innovations in Today’s World 269Rajaram P., Rajasekar Rangasamy, R. C. Karpagalakshmi, J. Lenin and S. Muthulingam13.1 Introduction 27013.2 Understanding the Motivations Behind Cyber Attacks: Financial, Political, and Military Goals 27213.3 Types of Cyber Threats: From Viruses to Data Breaches 27613.4 Impact of Cyber Attacks on Businesses and Governments: Financial and Operational Consequences 27813.5 Strategies for Cyber Security: Prevention, Detection, and Response 28113.6 Evolving Threat Landscape: Keeping Pace with Emerging Cyber Threats 28313.7 Exploring Global Cyber Security Initiatives: Collaborative Efforts and Best Practices 28513.8 Cyber Security Frameworks: Origins, Evolution, and Effectiveness 28613.9 Emerging Trends in Cyber Security: AI, Blockchain, and IoT Solutions 28813.10 Challenges and Limitations of Current Cyber Security Approaches 28913.11 Future Directions in Cyber Security Research and Development 29113.12 Conclusion 293References 29314 Predicting Android Ransomware Attacks Using Categorical Classification 295A. Pandiaraj, N. Ramshankar, Mathan Kumar Mounagurusamy, Karakanapati Mrudhula, P. Lahari Sai and Lekkala Likhitha14.1 Introduction 29614.2 Background Study 29714.3 Scope 30014.4 Experimentation 30014.5 Methodology 30314.6 Conclusion 306References 30615 Defense Strategies for Cognitive Cyber-Physical Systems in Machine Learning Domain 309M. Karthiga, N. Sangavi, V. R. Kiruthika, S. N. Sangeethaa, P. Ananthi and S. Vaanathi15.1 Introduction 31015.1.1 Background and Motivation 31315.1.2 Challenges in CPS Defense 31415.1.2.1 Resource Constraints and Real-Time Demands: Security in a Tight Spot 31415.1.2.2 Data Security and Privacy: Balancing Protection with User Rights 31415.1.2.3 Human Factors and Insider Threats: The Weakest Link 31515.1.2.4 Evolving Threats: A Never-Ending Battle 31515.2 Literature Review 31515.3 CPS Security Fears 31815.3.1 Vulnerabilities Posed in CPS 31915.4 Secure Approaches for CPS: In Terms of Technology and Attack Perspectives 32015.4.1 Security Strategies for Various Aspects of Attacks 32015.5 Issues and Concerns for Ml Protection for CPS 32215.5.1 ml Model Attacks and the Relevant Measures for Prevention 32315.5.1.1 Dataset Poisoning Attacks 32515.5.1.2 Black-Box Attack 32715.5.1.3 White Box Attack 32815.5.1.4 Backdoor Attacks 32815.6 Countermeasures Against Dataset Poisoning Attempts 32815.6.1 Simulated Poisoning Incidents 32915.6.2 Countermeasures Against Model Poisoning Incidents 33015.7 Vulnerability to Privacy 33015.7.1 Process of Reverse Engineering and API Calls Disclosing Sensitive Data 33115.8 Membership Inference Assaults 33315.9 Runtime Disruption Assault 33515.10 Comparative Investigation 33615.11 Conclusion and Future Research Directions 338References 33916 Cyber-Physical Systems: Challenges, Opportunities, Security Solutions 343Gopinathan S., S. Babu and P. Shanmugam16.1 Cyber-Physical Systems 34416.1.1 Introduction 34416.1.2 Present Issues on Cyber Security 34516.1.2.1 Phishing Exploits 34616.1.2.2 Internet of Things Ransomware 34716.1.2.3 Strengthened Regulation of Data Privacy 34716.1.2.4 Cyberattacks Using Mobile Technology 34716.1.2.5 A Higher Allocation of Resources to Automation 34716.1.3 CPS –Applications and Research Areas 34816.2 Cyber Security Challenges 35116.2.1 Social Media Role in Cyber-Security 35216.2.2 Cyber-Security Methods 35216.2.2.1 Access Management and Passphrase Protection 35216.2.2.2 Verification of Data 35216.2.2.3 Malicious Software Detectors 35316.2.2.4 Network Security Barriers 35316.2.2.5 Antimalware 35316.3 Integration of Physical and Digital 35316.3.1 Materials and Procedures 35416.3.2 Applications 35516.3.2.1 Financial Sector 35516.3.2.2 Health Division 35516.3.2.3 Business Sector 35616.3.2.4 Industry Sector 35616.4 Digital Threats to Physical Systems 35716.4.1 Threats Prioritization 35716.4.2 Selection of Security Requirements 35816.5 Industry 4.0 Security 35916.5.1 Classification of Cyber-Physical Systems and their Pertinent Themes within the Framework of Industry 4.0 36016.5.2 The Digital Supply System 36116.5.2.1 The Data Sharing Hazards Associated with the Digital Supply System 36116.5.2.2 Data Sharing: Granted Access to Information for More Parties 36216.5.3 Cybersecurity Challenges in Industry 4.0 36316.6 Evaluation of Risk for CPS 36416.6.1 Safety Risk Assessment Standards 36416.6.2 Approaches for Safety Risk Evaluation in CPS 36516.6.2.1 Analysis of Fault Trees 36516.6.2.2 Failure Modes and Impacts Evaluation (fmie) 36516.6.2.3 The Menace and Operability Approach 36516.6.2.4 Model-Centred Engineering 36616.6.2.5 Master Logic Illustration with Objective Tree - Accomplishment Tree 36616.6.2.6 System Theoretical Accident Model and Procedures (STAMP) is the Foundation for System Theoretic Process Analysis, a Hazard Analysis Method 366References 366Index 369

K. Ananthajothi, PhD is a Professor in the Department of Computer Science and Engineering at Rajalakshmi Engineering College, Chennai, India. He has published one book, two patents, and several research papers in international journals and conferences. His research focuses on machine learning and deep learning. S. N. Sangeethaa, PhD is a Professor in the Department of Computer Science and Engineering at the Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India. She has published seven books, more than 25 research articles in reputable journals, and more than 50 papers in national and international conferences. Her research interests include artificial intelligence, machine learning, and image processing. D. Divya, PhD is an Assistant Professor in the Department of Computer Science and Engineering at Misrimal Navajee Munoth Jain Engineering College, Chennai, India. She has published several papers in international journals. Her research focuses on data mining and machine learning. S. Balamurugan, PhD is the Director of Albert Einstein Engineering and Research Labs and the Vice-Chairman of the Renewable Energy Society of India. He has published more than 60 books, 300 articles in national and international journals and conferences, and 200 patents. His research interests include artificial intelligence, augmented reality, Internet of Things, big data analytics, cloud computing, and wearable computing. Sheng-Lung Peng, PhD is a Professor and the Director of the Department of Creative Technologies and Product Design at the National Taipei University of Business, Taiwan. He has published more than 100 research papers in addition to his role as a visiting professor and board member for several international universities and academic groups. His research interests include designing and analyzing algorithms for bioinformatics, combinatorics, data mining, and networks.

• Preface xvii1 Enhancing Safety and Security in Autonomous Connected Vehicles: Fusion of Optimal Control With Multi-Armed Bandit Learning 1K.T. Meena Abarna, A. Punitha and S. Sathiya1.1 Background 21.1.1 Problem Statement 41.1.2 Motivation 41.2 Related Works 51.2.1 Contributions 71.2.2 Centralized CRN Scheduling 81.2.3 Multi-Armed Bandit (MAB) 91.2.4 Bandit Learning with Switching Costs 111.3 System Model 121.3.1 Resource Spectrum 121.3.2 CRs’ Spectrum Utilization Schemes 131.3.3 CBS Scheduling 131.3.4 PUs’ Activity 131.4 Outcomes 151.4.1 Scenario I: Fallen Traffic Signs 151.4.2 Scenario II: Traffic Signs Alert by the Road Workers 161.4.3 Scenario III: Back/Rotated Traffic Sign Across the Road 171.4.4 Scenario IV: Hacking of a Stop Sign at a Four-Way Stop Intersection 181.5 Conclusions and Future Enhancement 191.5.1 Conclusions 191.5.2 Future Directions 21References 232 Secure Data Handling in AI and Proactive Response Network: Create a Physical Layer–Proposed Cognitive Cyber-Physical Security 25A. Sivasundari, P. Kumar, S. Vinodhkumar and N. Duraimurugan2.1 Introduction 262.1.1 The Role of AI in Cybersecurity 272.1.2 Usage of CCPS in IoT 272.2 Challenges and Mechanisms 282.2.1 Brief Account of Challenges Faced 282.2.2 Innovative Mechanisms 302.3 Using AI to Support Cognitive Cybersecurity 302.3.1 Cognitive Systems 302.3.2 AI in IoT 302.4 Create a Physical Layer–Proposed CCPS 312.4.1 Create a Physical Layer–Proposed CCPS in Healthcare Application 332.4.1.1 Privacy-Aware Collaboration 332.4.1.2 Cycle Model of CCPS 362.4.1.3 Dynamic Security Knowledge Base 362.4.2 Method for Secure Data Handling 362.5 Road Map of Implementation 382.5.1 AI for CCPS-IoT 382.5.2 AI-Enabled Wireless CCPS-IoT to Provide Security 392.6 Conclusions and Future Enhancement 40Future Directions 41References 433 Intelligent Cognitive Cyber-Physical System–Based Intrusion Detection for AI-Enabled Security in Industry 4.0 45V. Mahavaishnavi, R. Saminathan and G. Ramachandran3.1 Introduction 463.1.1 Cyber-Physical Systems 463.1.2 Intelligent Cyber-Physical Systems (ISPS) 473.1.3 Cognitive Cyber-Physical Systems (CCPS) 483.1.4 IDS in Industry 4.0 Using iCCPS 493.1.5 AI in iCCPS-IDS 493.2 Problem Statement 503.3 Motivation 513.4 Research Gap 523.5 Methodology 533.5.1 Training Dataset 543.5.2 Information for Assessment and Instruction 543.5.3 Model 543.5.4 CPS Determined by Cognition Agents 563.5.5 Useful Implementation of the Actual Device 573.6 Importance and Impact of AI-Based Intrusion Detection in iCCPS in Industry 4.0 593.6.1 Need 593.6.2 Challenges 603.7 Conclusions and Future Directions 60Future Directions 61References 634 Resilient Cognitive Cyber-Physical Systems: Conceptual Frameworks, Models, and Implementation Strategies 65R. Manivannan and M.P. Vaishnnave4.1 Introduction 664.1.1 Problem Statement 704.1.2 Motivation 714.2 Materials and Methods 724.3 CCPS Design Challenges 744.4 Cyber-Physical Systems Principles and Paradigms 774.4.1 CCPS Conceptual Framework 794.4.2 CCPS Modeling 814.4.3 Other Modeling Issues in CCPS 824.5 Conclusions and Future Enhancements 834.5.1 Future Enhancements 83References 855 Cognitive Cyber-Physical Security Challenges, Issues, and Recent Trends Over IoT 87Chinnaraj Govindasamy5.1 Introduction 885.1.1 From IoT to CCPS-IoT 935.1.2 Fundamental Cognitive Tasks 945.2 Motivation and Challenges 945.2.1 Motivation 945.2.2 Challenges 955.3 Security 965.3.1 Physical Layer Attacks 985.3.2 Physical Layer Security 995.3.3 Main Constituents 1005.4 Research Gap 1025.5 An Automatic Security Manager for CCPS Using IoT 1035.5.1 Combatting Erroneous Estimations 1035.5.2 Detection and Classification 1045.6 Conclusions and Future Enhancement 104Future Enhancement 105References 1066 Cognitive Cyber-Physical Security With IoT: A Solution to Smart Healthcare System 109P. Shanmugam, Mohamed Iqbal M. and M. Amanullah6.1 Introduction 1106.1.1 Motivation 1126.1.2 Need and Contribution 1136.1.2.1 Need 1136.1.2.2 Contribution 1146.2 Medical CCPS with IoT 1166.2.1 IoT Device for AI Solution 1186.2.2 Traditional Bio-Modality Spoofing Detection 1196.2.3 MCPS Using AI Device 1196.3 Functional and Behavioral Perspectives 1206.4 Modeling and Verification Methods of MCPS 1236.4.1 MCPS Modeling Based on ICE 1246.4.2 MCPS Modeling Based on Component 1256.5 Artificial Intelligence for Cognitive Cybersecurity 1256.5.1 Privacy-Aware Collaboration 1276.5.2 Cognitive Security Cycle Model 1276.6 Conclusions and Future Direction 1286.6.1 Conclusions 1286.6.2 Future Directions 129References 1307 Cognitive Cyber-Physical Security with IoT and ML: Role of Cybersecurity, Threats, and Benefits to Modern Economies and Industries 133P. Anbalagan, A. Kanthimathinathan and S. Saravanan7.1 Introduction 1347.1.1 Key Contributions 1367.1.2 Problem Statement 1377.1.3 Motivation 1387.2 CCPS Associated with IoT 1397.2.1 Reasons in Favor of Cognitive Analytics 1407.2.2 Analyses of Current Cyber Risk Data 1417.3 Materials and Methods 1437.3.1 Role of Cybersecurity in CCPS with IoT and ml 1437.3.2 ml in Cognitive Cyber-Physical Security with IoT 1447.3.3 Threats to Modern Economies and Industries 1447.3.4 Benefits to Modern Economies and Industries 1477.4 Outcomes 1487.4.1 AI-Enabled Management Technology and Approach Taxonomy 1517.4.2 Essential Self-Adapting System Technologies 1517.4.3 Attack Malware Classifier 1517.5 Conclusions and Future Direction 152Future Directions 152References 1548 A Safety Analysis Framework for Medical Cyber-Physical Systems Using Systems Theory 157K. Ananthajothi, K. Balamurugan, D. Divya and T.P. Latchoumi8.1 Introduction 1588.2 Background 1608.2.1 Cyber-Physical Systems 1608.2.2 Quality-of-Service Issues in CPS 1618.2.3 Medical Cyber-Physical Systems 1618.3 The Systems-Based Safety Analysis Observation for MCPS 1628.3.1 Identification of Critical Requirements in MCPS 1628.3.2 A Systems Theory–Based Method for Safety Analysis in Medical Cyber-Physical Systems 1638.3.3 MCPS in Patient-Controlled Analgesia 1658.4 Improved Wireless Medical Cyber-Physical System (IWMCPS) 1668.4.1 Level: Data Acquisition 1668.4.2 Layer: Data Aggregating 1678.4.3 Level: Storing 1678.4.4 Level: Action 1688.4.5 IWMCPS Architectural Research 1688.4.6 Core of Communications and Sensors 1688.5 Hazard Analysis on PCA-MCPS 1698.5.1 System Safety Constraint 1708.5.2 System Safety Control Structure 1708.5.3 Identify Unsafe Control Actions 1708.5.4 Specifying Causes 1718.6 Conclusions and Future Directions 172Future Directions 172References 1749 Cognitive Cybersecurity and Reinforcement Learning: Enhancing Security in CPS-IoT Enabled Healthcare 177A. Arokiaraj Jovith, M. Sangeetha, D. Saveetha and S. Antelin Vijila9.1 Introduction 1789.2 Methodology 1829.2.1 Device AI Solutions 1829.2.2 Detect the Spoofing of Bio-Modality 1829.2.3 Detect the Spoofing of Bio-Modality Using Machine Learning 1839.3 Challenges and Mechanisms 1839.3.1 Challenges 1839.3.2 Innovative Mechanisms 1859.4 Cognitive Cyber-Physical Systems and Reinforcement Learning 1859.4.1 Model Formulation 1889.4.2 AI in CCPS 1899.4.2.1 Privacy-Aware Collaboration 1929.4.2.2 Cognitive Security Cycle Model 1929.4.2.3 Need 1939.4.2.4 Cross-Sectoral Techniques 1939.4.2.5 Actuation and Data Collection 1949.5 Conclusions and Future Directions 1949.6 Future Directions 195References 19610 Navigating the Digital Landscape: Understanding, Detecting, and Mitigating Cyber Threats in an Evolving Technological Era 199Manikandan J., Hemalatha P., Jayashree K. and Rajeswari P.10.1 The Digital Transformation: Shaping Modern Business Dynamics 20010.2 Impact of COVID-19: Accelerating the Digital Shift 20110.3 Online Safety Concerns: Navigating the Digital Landscape 20210.4 Interplay of Digital Technologies: Vulnerabilities and Threats 20410.4.1 Introduction to Digital Technologies 20410.4.2 Case Studies and Examples 20610.5 Rise of Cyber Assaults as a Service: Automating Criminal Activities 20710.6 Evolving Threat Landscape: Understanding Modern Cyber Attacks 21010.7 Beyond Conventional Security Measures: The Need for Advanced Defense 21110.8 Rise of Cyber Assaults as a Service: Automating Criminal Activities 21310.8.1 Introduction to Cyber Assaults as a Service 21310.8.2 Automation of Criminal Activities 21310.8.3 Impact and Implications 21410.9 Evolving Threat Landscape: Understanding Modern Cyber Attacks 21510.9.1 Types of Modern Cyber Attacks 21510.9.2 Implications for Cybersecurity Defense 21610.10 Beyond Conventional Security Measures: The Need for Advanced Defense 21710.10.1 Challenges with Conventional Security Measures 21710.10.2 The Evolution of Advanced Defense 21810.11 Uncovering Cyber Threats: Patterns, Trends, and Detection Methods 21810.11.1 Patterns of Cyber Threats 21810.12 Addressing Advanced Persistent Threats: Challenges and Solutions 22010.12.1 Introduction to Advanced Persistent Threats (APTs) 22010.12.2 Challenges Posed by APTs 22010.12.3 Solutions for Addressing APTs 221References 22211 Defense Strategies for Cyber-Physical Systems 225Rajendran Thanikachalam, T. Nithya, Balaji Sampathkumar and J. Mangayarkarasi11.1 Introduction 22611.2 Threat Landscape in CPS 22811.3 Advanced Defense Strategies 23111.3.1 Anomaly Detection in CPS 23111.3.2 Secure Communication Protocols 23211.3.3 Machine Learning-Driven Defenses 23511.3.4 Zero Trust Model for CPS 23711.3.5 Resilience Techniques for CPS 24011.3.6 Intensive Training and Awareness 24111.3.7 Conclusion and Future Directions 245References 24512 Cybersecurity in the Era of Artificial Intelligence: Challenges and Innovations 249Ashwini A., H. Sehina and Banu Priya Prathaban12.1 Introduction to Cybersecurity Analysis 25012.2 Need for AI in Cybersecurity 25212.3 Current Cybersecurity Techniques 25312.4 Role of AI in Cybersecurity 25512.5 Challenges in AI Enhanced Cybersecurity 25612.6 Quantum Computing and Post Quantum Computing in Cybersecurity 25712.7 AI Powered Encryption Analysis 25912.8 Adaptive Cybersecurity 26112.9 Overall Analysis of AI in Cybersecurity 26212.10 Privacy Preserving AI and Cybersecurity 26312.11 Future Directions and Research Challenges 26412.12 Conclusion 266References 26613 Safeguarding the Virtual Realm: Assessing Cyber Security Challenges and Innovations in Today’s World 269Rajaram P., Rajasekar Rangasamy, R. C. Karpagalakshmi, J. Lenin and S. Muthulingam13.1 Introduction 27013.2 Understanding the Motivations Behind Cyber Attacks: Financial, Political, and Military Goals 27213.3 Types of Cyber Threats: From Viruses to Data Breaches 27613.4 Impact of Cyber Attacks on Businesses and Governments: Financial and Operational Consequences 27813.5 Strategies for Cyber Security: Prevention, Detection, and Response 28113.6 Evolving Threat Landscape: Keeping Pace with Emerging Cyber Threats 28313.7 Exploring Global Cyber Security Initiatives: Collaborative Efforts and Best Practices 28513.8 Cyber Security Frameworks: Origins, Evolution, and Effectiveness 28613.9 Emerging Trends in Cyber Security: AI, Blockchain, and IoT Solutions 28813.10 Challenges and Limitations of Current Cyber Security Approaches 28913.11 Future Directions in Cyber Security Research and Development 29113.12 Conclusion 293References 29314 Predicting Android Ransomware Attacks Using Categorical Classification 295A. Pandiaraj, N. Ramshankar, Mathan Kumar Mounagurusamy, Karakanapati Mrudhula, P. Lahari Sai and Lekkala Likhitha14.1 Introduction 29614.2 Background Study 29714.3 Scope 30014.4 Experimentation 30014.5 Methodology 30314.6 Conclusion 306References 30615 Defense Strategies for Cognitive Cyber-Physical Systems in Machine Learning Domain 309M. Karthiga, N. Sangavi, V. R. Kiruthika, S. N. Sangeethaa, P. Ananthi and S. Vaanathi15.1 Introduction 31015.1.1 Background and Motivation 31315.1.2 Challenges in CPS Defense 31415.1.2.1 Resource Constraints and Real-Time Demands: Security in a Tight Spot 31415.1.2.2 Data Security and Privacy: Balancing Protection with User Rights 31415.1.2.3 Human Factors and Insider Threats: The Weakest Link 31515.1.2.4 Evolving Threats: A Never-Ending Battle 31515.2 Literature Review 31515.3 CPS Security Fears 31815.3.1 Vulnerabilities Posed in CPS 31915.4 Secure Approaches for CPS: In Terms of Technology and Attack Perspectives 32015.4.1 Security Strategies for Various Aspects of Attacks 32015.5 Issues and Concerns for Ml Protection for CPS 32215.5.1 ml Model Attacks and the Relevant Measures for Prevention 32315.5.1.1 Dataset Poisoning Attacks 32515.5.1.2 Black-Box Attack 32715.5.1.3 White Box Attack 32815.5.1.4 Backdoor Attacks 32815.6 Countermeasures Against Dataset Poisoning Attempts 32815.6.1 Simulated Poisoning Incidents 32915.6.2 Countermeasures Against Model Poisoning Incidents 33015.7 Vulnerability to Privacy 33015.7.1 Process of Reverse Engineering and API Calls Disclosing Sensitive Data 33115.8 Membership Inference Assaults 33315.9 Runtime Disruption Assault 33515.10 Comparative Investigation 33615.11 Conclusion and Future Research Directions 338References 33916 Cyber-Physical Systems: Challenges, Opportunities, Security Solutions 343Gopinathan S., S. Babu and P. Shanmugam16.1 Cyber-Physical Systems 34416.1.1 Introduction 34416.1.2 Present Issues on Cyber Security 34516.1.2.1 Phishing Exploits 34616.1.2.2 Internet of Things Ransomware 34716.1.2.3 Strengthened Regulation of Data Privacy 34716.1.2.4 Cyberattacks Using Mobile Technology 34716.1.2.5 A Higher Allocation of Resources to Automation 34716.1.3 CPS –Applications and Research Areas 34816.2 Cyber Security Challenges 35116.2.1 Social Media Role in Cyber-Security 35216.2.2 Cyber-Security Methods 35216.2.2.1 Access Management and Passphrase Protection 35216.2.2.2 Verification of Data 35216.2.2.3 Malicious Software Detectors 35316.2.2.4 Network Security Barriers 35316.2.2.5 Antimalware 35316.3 Integration of Physical and Digital 35316.3.1 Materials and Procedures 35416.3.2 Applications 35516.3.2.1 Financial Sector 35516.3.2.2 Health Division 35516.3.2.3 Business Sector 35616.3.2.4 Industry Sector 35616.4 Digital Threats to Physical Systems 35716.4.1 Threats Prioritization 35716.4.2 Selection of Security Requirements 35816.5 Industry 4.0 Security 35916.5.1 Classification of Cyber-Physical Systems and their Pertinent Themes within the Framework of Industry 4.0 36016.5.2 The Digital Supply System 36116.5.2.1 The Data Sharing Hazards Associated with the Digital Supply System 36116.5.2.2 Data Sharing: Granted Access to Information for More Parties 36216.5.3 Cybersecurity Challenges in Industry 4.0 36316.6 Evaluation of Risk for CPS 36416.6.1 Safety Risk Assessment Standards 36416.6.2 Approaches for Safety Risk Evaluation in CPS 36516.6.2.1 Analysis of Fault Trees 36516.6.2.2 Failure Modes and Impacts Evaluation (fmie) 36516.6.2.3 The Menace and Operability Approach 36516.6.2.4 Model-Centred Engineering 36616.6.2.5 Master Logic Illustration with Objective Tree - Accomplishment Tree 36616.6.2.6 System Theoretical Accident Model and Procedures (STAMP) is the Foundation for System Theoretic Process Analysis, a Hazard Analysis Method 366References 366Index 369

K. Ananthajothi, PhD is a Professor in the Department of Computer Science and Engineering at Rajalakshmi Engineering College, Chennai, India. He has published one book, two patents, and several research papers in international journals and conferences. His research focuses on machine learning and deep learning. S. N. Sangeethaa, PhD is a Professor in the Department of Computer Science and Engineering at the Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India. She has published seven books, more than 25 research articles in reputable journals, and more than 50 papers in national and international conferences. Her research interests include artificial intelligence, machine learning, and image processing. D. Divya, PhD is an Assistant Professor in the Department of Computer Science and Engineering at Misrimal Navajee Munoth Jain Engineering College, Chennai, India. She has published several papers in international journals. Her research focuses on data mining and machine learning. S. Balamurugan, PhD is the Director of Albert Einstein Engineering and Research Labs and the Vice-Chairman of the Renewable Energy Society of India. He has published more than 60 books, 300 articles in national and international journals and conferences, and 200 patents. His research interests include artificial intelligence, augmented reality, Internet of Things, big data analytics, cloud computing, and wearable computing. Sheng-Lung Peng, PhD is a Professor and the Director of the Department of Creative Technologies and Product Design at the National Taipei University of Business, Taiwan. He has published more than 100 research papers in addition to his role as a visiting professor and board member for several international universities and academic groups. His research interests include designing and analyzing algorithms for bioinformatics, combinatorics, data mining, and networks.

• Preface xvii1 Enhancing Safety and Security in Autonomous Connected Vehicles: Fusion of Optimal Control With Multi-Armed Bandit Learning 1K.T. Meena Abarna, A. Punitha and S. Sathiya1.1 Background 21.1.1 Problem Statement 41.1.2 Motivation 41.2 Related Works 51.2.1 Contributions 71.2.2 Centralized CRN Scheduling 81.2.3 Multi-Armed Bandit (MAB) 91.2.4 Bandit Learning with Switching Costs 111.3 System Model 121.3.1 Resource Spectrum 121.3.2 CRs’ Spectrum Utilization Schemes 131.3.3 CBS Scheduling 131.3.4 PUs’ Activity 131.4 Outcomes 151.4.1 Scenario I: Fallen Traffic Signs 151.4.2 Scenario II: Traffic Signs Alert by the Road Workers 161.4.3 Scenario III: Back/Rotated Traffic Sign Across the Road 171.4.4 Scenario IV: Hacking of a Stop Sign at a Four-Way Stop Intersection 181.5 Conclusions and Future Enhancement 191.5.1 Conclusions 191.5.2 Future Directions 21References 232 Secure Data Handling in AI and Proactive Response Network: Create a Physical Layer–Proposed Cognitive Cyber-Physical Security 25A. Sivasundari, P. Kumar, S. Vinodhkumar and N. Duraimurugan2.1 Introduction 262.1.1 The Role of AI in Cybersecurity 272.1.2 Usage of CCPS in IoT 272.2 Challenges and Mechanisms 282.2.1 Brief Account of Challenges Faced 282.2.2 Innovative Mechanisms 302.3 Using AI to Support Cognitive Cybersecurity 302.3.1 Cognitive Systems 302.3.2 AI in IoT 302.4 Create a Physical Layer–Proposed CCPS 312.4.1 Create a Physical Layer–Proposed CCPS in Healthcare Application 332.4.1.1 Privacy-Aware Collaboration 332.4.1.2 Cycle Model of CCPS 362.4.1.3 Dynamic Security Knowledge Base 362.4.2 Method for Secure Data Handling 362.5 Road Map of Implementation 382.5.1 AI for CCPS-IoT 382.5.2 AI-Enabled Wireless CCPS-IoT to Provide Security 392.6 Conclusions and Future Enhancement 40Future Directions 41References 433 Intelligent Cognitive Cyber-Physical System–Based Intrusion Detection for AI-Enabled Security in Industry 4.0 45V. Mahavaishnavi, R. Saminathan and G. Ramachandran3.1 Introduction 463.1.1 Cyber-Physical Systems 463.1.2 Intelligent Cyber-Physical Systems (ISPS) 473.1.3 Cognitive Cyber-Physical Systems (CCPS) 483.1.4 IDS in Industry 4.0 Using iCCPS 493.1.5 AI in iCCPS-IDS 493.2 Problem Statement 503.3 Motivation 513.4 Research Gap 523.5 Methodology 533.5.1 Training Dataset 543.5.2 Information for Assessment and Instruction 543.5.3 Model 543.5.4 CPS Determined by Cognition Agents 563.5.5 Useful Implementation of the Actual Device 573.6 Importance and Impact of AI-Based Intrusion Detection in iCCPS in Industry 4.0 593.6.1 Need 593.6.2 Challenges 603.7 Conclusions and Future Directions 60Future Directions 61References 634 Resilient Cognitive Cyber-Physical Systems: Conceptual Frameworks, Models, and Implementation Strategies 65R. Manivannan and M.P. Vaishnnave4.1 Introduction 664.1.1 Problem Statement 704.1.2 Motivation 714.2 Materials and Methods 724.3 CCPS Design Challenges 744.4 Cyber-Physical Systems Principles and Paradigms 774.4.1 CCPS Conceptual Framework 794.4.2 CCPS Modeling 814.4.3 Other Modeling Issues in CCPS 824.5 Conclusions and Future Enhancements 834.5.1 Future Enhancements 83References 855 Cognitive Cyber-Physical Security Challenges, Issues, and Recent Trends Over IoT 87Chinnaraj Govindasamy5.1 Introduction 885.1.1 From IoT to CCPS-IoT 935.1.2 Fundamental Cognitive Tasks 945.2 Motivation and Challenges 945.2.1 Motivation 945.2.2 Challenges 955.3 Security 965.3.1 Physical Layer Attacks 985.3.2 Physical Layer Security 995.3.3 Main Constituents 1005.4 Research Gap 1025.5 An Automatic Security Manager for CCPS Using IoT 1035.5.1 Combatting Erroneous Estimations 1035.5.2 Detection and Classification 1045.6 Conclusions and Future Enhancement 104Future Enhancement 105References 1066 Cognitive Cyber-Physical Security With IoT: A Solution to Smart Healthcare System 109P. Shanmugam, Mohamed Iqbal M. and M. Amanullah6.1 Introduction 1106.1.1 Motivation 1126.1.2 Need and Contribution 1136.1.2.1 Need 1136.1.2.2 Contribution 1146.2 Medical CCPS with IoT 1166.2.1 IoT Device for AI Solution 1186.2.2 Traditional Bio-Modality Spoofing Detection 1196.2.3 MCPS Using AI Device 1196.3 Functional and Behavioral Perspectives 1206.4 Modeling and Verification Methods of MCPS 1236.4.1 MCPS Modeling Based on ICE 1246.4.2 MCPS Modeling Based on Component 1256.5 Artificial Intelligence for Cognitive Cybersecurity 1256.5.1 Privacy-Aware Collaboration 1276.5.2 Cognitive Security Cycle Model 1276.6 Conclusions and Future Direction 1286.6.1 Conclusions 1286.6.2 Future Directions 129References 1307 Cognitive Cyber-Physical Security with IoT and ML: Role of Cybersecurity, Threats, and Benefits to Modern Economies and Industries 133P. Anbalagan, A. Kanthimathinathan and S. Saravanan7.1 Introduction 1347.1.1 Key Contributions 1367.1.2 Problem Statement 1377.1.3 Motivation 1387.2 CCPS Associated with IoT 1397.2.1 Reasons in Favor of Cognitive Analytics 1407.2.2 Analyses of Current Cyber Risk Data 1417.3 Materials and Methods 1437.3.1 Role of Cybersecurity in CCPS with IoT and ml 1437.3.2 ml in Cognitive Cyber-Physical Security with IoT 1447.3.3 Threats to Modern Economies and Industries 1447.3.4 Benefits to Modern Economies and Industries 1477.4 Outcomes 1487.4.1 AI-Enabled Management Technology and Approach Taxonomy 1517.4.2 Essential Self-Adapting System Technologies 1517.4.3 Attack Malware Classifier 1517.5 Conclusions and Future Direction 152Future Directions 152References 1548 A Safety Analysis Framework for Medical Cyber-Physical Systems Using Systems Theory 157K. Ananthajothi, K. Balamurugan, D. Divya and T.P. Latchoumi8.1 Introduction 1588.2 Background 1608.2.1 Cyber-Physical Systems 1608.2.2 Quality-of-Service Issues in CPS 1618.2.3 Medical Cyber-Physical Systems 1618.3 The Systems-Based Safety Analysis Observation for MCPS 1628.3.1 Identification of Critical Requirements in MCPS 1628.3.2 A Systems Theory–Based Method for Safety Analysis in Medical Cyber-Physical Systems 1638.3.3 MCPS in Patient-Controlled Analgesia 1658.4 Improved Wireless Medical Cyber-Physical System (IWMCPS) 1668.4.1 Level: Data Acquisition 1668.4.2 Layer: Data Aggregating 1678.4.3 Level: Storing 1678.4.4 Level: Action 1688.4.5 IWMCPS Architectural Research 1688.4.6 Core of Communications and Sensors 1688.5 Hazard Analysis on PCA-MCPS 1698.5.1 System Safety Constraint 1708.5.2 System Safety Control Structure 1708.5.3 Identify Unsafe Control Actions 1708.5.4 Specifying Causes 1718.6 Conclusions and Future Directions 172Future Directions 172References 1749 Cognitive Cybersecurity and Reinforcement Learning: Enhancing Security in CPS-IoT Enabled Healthcare 177A. Arokiaraj Jovith, M. Sangeetha, D. Saveetha and S. Antelin Vijila9.1 Introduction 1789.2 Methodology 1829.2.1 Device AI Solutions 1829.2.2 Detect the Spoofing of Bio-Modality 1829.2.3 Detect the Spoofing of Bio-Modality Using Machine Learning 1839.3 Challenges and Mechanisms 1839.3.1 Challenges 1839.3.2 Innovative Mechanisms 1859.4 Cognitive Cyber-Physical Systems and Reinforcement Learning 1859.4.1 Model Formulation 1889.4.2 AI in CCPS 1899.4.2.1 Privacy-Aware Collaboration 1929.4.2.2 Cognitive Security Cycle Model 1929.4.2.3 Need 1939.4.2.4 Cross-Sectoral Techniques 1939.4.2.5 Actuation and Data Collection 1949.5 Conclusions and Future Directions 1949.6 Future Directions 195References 19610 Navigating the Digital Landscape: Understanding, Detecting, and Mitigating Cyber Threats in an Evolving Technological Era 199Manikandan J., Hemalatha P., Jayashree K. and Rajeswari P.10.1 The Digital Transformation: Shaping Modern Business Dynamics 20010.2 Impact of COVID-19: Accelerating the Digital Shift 20110.3 Online Safety Concerns: Navigating the Digital Landscape 20210.4 Interplay of Digital Technologies: Vulnerabilities and Threats 20410.4.1 Introduction to Digital Technologies 20410.4.2 Case Studies and Examples 20610.5 Rise of Cyber Assaults as a Service: Automating Criminal Activities 20710.6 Evolving Threat Landscape: Understanding Modern Cyber Attacks 21010.7 Beyond Conventional Security Measures: The Need for Advanced Defense 21110.8 Rise of Cyber Assaults as a Service: Automating Criminal Activities 21310.8.1 Introduction to Cyber Assaults as a Service 21310.8.2 Automation of Criminal Activities 21310.8.3 Impact and Implications 21410.9 Evolving Threat Landscape: Understanding Modern Cyber Attacks 21510.9.1 Types of Modern Cyber Attacks 21510.9.2 Implications for Cybersecurity Defense 21610.10 Beyond Conventional Security Measures: The Need for Advanced Defense 21710.10.1 Challenges with Conventional Security Measures 21710.10.2 The Evolution of Advanced Defense 21810.11 Uncovering Cyber Threats: Patterns, Trends, and Detection Methods 21810.11.1 Patterns of Cyber Threats 21810.12 Addressing Advanced Persistent Threats: Challenges and Solutions 22010.12.1 Introduction to Advanced Persistent Threats (APTs) 22010.12.2 Challenges Posed by APTs 22010.12.3 Solutions for Addressing APTs 221References 22211 Defense Strategies for Cyber-Physical Systems 225Rajendran Thanikachalam, T. Nithya, Balaji Sampathkumar and J. Mangayarkarasi11.1 Introduction 22611.2 Threat Landscape in CPS 22811.3 Advanced Defense Strategies 23111.3.1 Anomaly Detection in CPS 23111.3.2 Secure Communication Protocols 23211.3.3 Machine Learning-Driven Defenses 23511.3.4 Zero Trust Model for CPS 23711.3.5 Resilience Techniques for CPS 24011.3.6 Intensive Training and Awareness 24111.3.7 Conclusion and Future Directions 245References 24512 Cybersecurity in the Era of Artificial Intelligence: Challenges and Innovations 249Ashwini A., H. Sehina and Banu Priya Prathaban12.1 Introduction to Cybersecurity Analysis 25012.2 Need for AI in Cybersecurity 25212.3 Current Cybersecurity Techniques 25312.4 Role of AI in Cybersecurity 25512.5 Challenges in AI Enhanced Cybersecurity 25612.6 Quantum Computing and Post Quantum Computing in Cybersecurity 25712.7 AI Powered Encryption Analysis 25912.8 Adaptive Cybersecurity 26112.9 Overall Analysis of AI in Cybersecurity 26212.10 Privacy Preserving AI and Cybersecurity 26312.11 Future Directions and Research Challenges 26412.12 Conclusion 266References 26613 Safeguarding the Virtual Realm: Assessing Cyber Security Challenges and Innovations in Today’s World 269Rajaram P., Rajasekar Rangasamy, R. C. Karpagalakshmi, J. Lenin and S. Muthulingam13.1 Introduction 27013.2 Understanding the Motivations Behind Cyber Attacks: Financial, Political, and Military Goals 27213.3 Types of Cyber Threats: From Viruses to Data Breaches 27613.4 Impact of Cyber Attacks on Businesses and Governments: Financial and Operational Consequences 27813.5 Strategies for Cyber Security: Prevention, Detection, and Response 28113.6 Evolving Threat Landscape: Keeping Pace with Emerging Cyber Threats 28313.7 Exploring Global Cyber Security Initiatives: Collaborative Efforts and Best Practices 28513.8 Cyber Security Frameworks: Origins, Evolution, and Effectiveness 28613.9 Emerging Trends in Cyber Security: AI, Blockchain, and IoT Solutions 28813.10 Challenges and Limitations of Current Cyber Security Approaches 28913.11 Future Directions in Cyber Security Research and Development 29113.12 Conclusion 293References 29314 Predicting Android Ransomware Attacks Using Categorical Classification 295A. Pandiaraj, N. Ramshankar, Mathan Kumar Mounagurusamy, Karakanapati Mrudhula, P. Lahari Sai and Lekkala Likhitha14.1 Introduction 29614.2 Background Study 29714.3 Scope 30014.4 Experimentation 30014.5 Methodology 30314.6 Conclusion 306References 30615 Defense Strategies for Cognitive Cyber-Physical Systems in Machine Learning Domain 309M. Karthiga, N. Sangavi, V. R. Kiruthika, S. N. Sangeethaa, P. Ananthi and S. Vaanathi15.1 Introduction 31015.1.1 Background and Motivation 31315.1.2 Challenges in CPS Defense 31415.1.2.1 Resource Constraints and Real-Time Demands: Security in a Tight Spot 31415.1.2.2 Data Security and Privacy: Balancing Protection with User Rights 31415.1.2.3 Human Factors and Insider Threats: The Weakest Link 31515.1.2.4 Evolving Threats: A Never-Ending Battle 31515.2 Literature Review 31515.3 CPS Security Fears 31815.3.1 Vulnerabilities Posed in CPS 31915.4 Secure Approaches for CPS: In Terms of Technology and Attack Perspectives 32015.4.1 Security Strategies for Various Aspects of Attacks 32015.5 Issues and Concerns for Ml Protection for CPS 32215.5.1 ml Model Attacks and the Relevant Measures for Prevention 32315.5.1.1 Dataset Poisoning Attacks 32515.5.1.2 Black-Box Attack 32715.5.1.3 White Box Attack 32815.5.1.4 Backdoor Attacks 32815.6 Countermeasures Against Dataset Poisoning Attempts 32815.6.1 Simulated Poisoning Incidents 32915.6.2 Countermeasures Against Model Poisoning Incidents 33015.7 Vulnerability to Privacy 33015.7.1 Process of Reverse Engineering and API Calls Disclosing Sensitive Data 33115.8 Membership Inference Assaults 33315.9 Runtime Disruption Assault 33515.10 Comparative Investigation 33615.11 Conclusion and Future Research Directions 338References 33916 Cyber-Physical Systems: Challenges, Opportunities, Security Solutions 343Gopinathan S., S. Babu and P. Shanmugam16.1 Cyber-Physical Systems 34416.1.1 Introduction 34416.1.2 Present Issues on Cyber Security 34516.1.2.1 Phishing Exploits 34616.1.2.2 Internet of Things Ransomware 34716.1.2.3 Strengthened Regulation of Data Privacy 34716.1.2.4 Cyberattacks Using Mobile Technology 34716.1.2.5 A Higher Allocation of Resources to Automation 34716.1.3 CPS –Applications and Research Areas 34816.2 Cyber Security Challenges 35116.2.1 Social Media Role in Cyber-Security 35216.2.2 Cyber-Security Methods 35216.2.2.1 Access Management and Passphrase Protection 35216.2.2.2 Verification of Data 35216.2.2.3 Malicious Software Detectors 35316.2.2.4 Network Security Barriers 35316.2.2.5 Antimalware 35316.3 Integration of Physical and Digital 35316.3.1 Materials and Procedures 35416.3.2 Applications 35516.3.2.1 Financial Sector 35516.3.2.2 Health Division 35516.3.2.3 Business Sector 35616.3.2.4 Industry Sector 35616.4 Digital Threats to Physical Systems 35716.4.1 Threats Prioritization 35716.4.2 Selection of Security Requirements 35816.5 Industry 4.0 Security 35916.5.1 Classification of Cyber-Physical Systems and their Pertinent Themes within the Framework of Industry 4.0 36016.5.2 The Digital Supply System 36116.5.2.1 The Data Sharing Hazards Associated with the Digital Supply System 36116.5.2.2 Data Sharing: Granted Access to Information for More Parties 36216.5.3 Cybersecurity Challenges in Industry 4.0 36316.6 Evaluation of Risk for CPS 36416.6.1 Safety Risk Assessment Standards 36416.6.2 Approaches for Safety Risk Evaluation in CPS 36516.6.2.1 Analysis of Fault Trees 36516.6.2.2 Failure Modes and Impacts Evaluation (fmie) 36516.6.2.3 The Menace and Operability Approach 36516.6.2.4 Model-Centred Engineering 36616.6.2.5 Master Logic Illustration with Objective Tree - Accomplishment Tree 36616.6.2.6 System Theoretical Accident Model and Procedures (STAMP) is the Foundation for System Theoretic Process Analysis, a Hazard Analysis Method 366References 366Index 369