Autorentext

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.

Klappentext

Protect critical infrastructure from emerging threats with this essential guide, providing an in-depth exploration of innovative defense strategies and practical solutions for securing cyber-physical systems. As industries increasingly rely on the convergence of digital and physical infrastructures, the need for robust cybersecurity solutions has grown. This book addresses the key challenges posed by integrating digital technologies into critical physical systems across various sectors, including energy, healthcare, and manufacturing. Focusing on innovative defence strategies and practical solutions, this book provides an in-depth exploration of the vulnerabilities and defence mechanisms essential to securing cyber-physical systems. The book is designed to equip researchers, cybersecurity professionals, and industry leaders with the knowledge to protect critical infrastructure from emerging digital threats. From understanding complex vulnerabilities to implementing secure system designs, this volume offers a comprehensive guide to fortifying and securing the systems that shape our modern, interconnected world. Readers will find the volume:

• Explores the evolving threat landscape, encompassing potential attacks on critical infrastructure, industrial systems, and interconnected devices;
• Examines vulnerabilities inherent in cyber-physical systems, such as weak access controls, insecure communication channels, and the susceptibility of physical components to digital manipulation;
• Uses real-world case studies to introduce strategies for assessing and quantifying the cybersecurity risks associated with cyber-physical systems, considering the potential consequences of system breaches;
• Provides an overview of cybersecurity measures and defense mechanisms designed to fortify cyber-physical systems against digital threats, including intrusion detection systems, encryption, and security best practices;

• Discusses existing and emerging regulatory frameworks aimed at enhancing cybersecurity in critical infrastructure and physical systems. Audience Researchers, cybersecurity professionals, information technologists and industry leaders innovating infrastructure to protect against digital threats.

  Inhalt

  Preface xvii

  1 Enhancing Safety and Security in Autonomous Connected Vehicles: Fusion of Optimal Control With Multi-Armed Bandit Learning 1
  K.T. Meena Abarna, A. Punitha and S. Sathiya

  1.1 Background 2

  1.1.1 Problem Statement 4

  1.1.2 Motivation 4

  1.2 Related Works 5

  1.2.1 Contributions 7

  1.2.2 Centralized CRN Scheduling 8

  1.2.3 Multi-Armed Bandit (MAB) 9

  1.2.4 Bandit Learning with Switching Costs 11

  1.3 System Model 12

  1.3.1 Resource Spectrum 12

  1.3.2 CRs' Spectrum Utilization Schemes 13

  1.3.3 CBS Scheduling 13

  1.3.4 PUs' Activity 13

  1.4 Outcomes 15

  1.4.1 Scenario I: Fallen Traffic Signs 15

  1.4.2 Scenario II: Traffic Signs Alert by the Road Workers 16

  1.4.3 Scenario III: Back/Rotated Traffic Sign Across the Road 17

  1.4.4 Scenario IV: Hacking of a Stop Sign at a Four-Way Stop Intersection 18

  1.5 Conclusions and Future Enhancement 19

  1.5.1 Conclusions 19

  1.5.2 Future Directions 21

  References 23

  2 Secure Data Handling in AI and Proactive Response Network: Create a Physical Layer-Proposed Cognitive Cyber-Physical Security 25
  A. Sivasundari, P. Kumar, S. Vinodhkumar and N. Duraimurugan

  2.1 Introduction 26

  2.1.1 The Role of AI in Cybersecurity 27

  2.1.2 Usage of CCPS in IoT 27

  2.2 Challenges and Mechanisms 28

  2.2.1 Brief Account of Challenges Faced 28

  2.2.2 Innovative Mechanisms 30

  2.3 Using AI to Support Cognitive Cybersecurity 30

  2.3.1 Cognitive Systems 30

  2.3.2 AI in IoT 30

  2.4 Create a Physical Layer-Proposed CCPS 31

  2.4.1 Create a Physical Layer-Proposed CCPS in Healthcare Application 33

  2.4.1.1 Privacy-Aware Collaboration 33

  2.4.1.2 Cycle Model of CCPS 36

  2.4.1.3 Dynamic Security Knowledge Base 36

  2.4.2 Method for Secure Data Handling 36

  2.5 Road Map of Implementation 38

  2.5.1 AI for CCPS-IoT 38

  2.5.2 AI-Enabled Wireless CCPS-IoT to Provide Security 39

  2.6 Conclusions and Future Enhancement 40

  Future Directions 41

  References 43

  3 Intelligent Cognitive Cyber-Physical System-Based Intrusion Detection for AI-Enabled Security in Industry 4.0 45
  V. Mahavaishnavi, R. Saminathan and G. Ramachandran

  3.1 Introduction 46

  3.1.1 Cyber-Physical Systems 46

  3.1.2 Intelligent Cyber-Physical Systems (ISPS) 47

  3.1.3 Cognitive Cyber-Physical Systems (CCPS) 48

  3.1.4 IDS in Industry 4.0 Using iCCPS 49

  3.1.5 AI in iCCPS-IDS 49

  3.2 Problem Statement 50

  3.3 Motivation 51

  3.4 Research Gap 52

  3.5 Methodology 53

  3.5.1 Training Dataset 54

  3.5.2 Information for Assessment and Instruction 54

  3.5.3 Model 54

  3.5.4 CPS Determined by Cognition Agents 56

  3.5.5 Useful Implementation of the Actual Device 57

  3.6 Importance and Impact of AI-Based Intrusion Detection in iCCPS in Industry 4.0 59

  3.6.1 Need 59

  3.6.2 Challenges 60

  3.7 Conclusions and Future Directions 60

  Future Directions 61

  References 63

  4 Resilient Cognitive Cyber-Physical Systems: Conceptual Frameworks, Models, and Implementation Strategies 65
  R. Manivannan and M.P. Vaishnnave

  4.1 Introduction 66

  4.1.1 Problem Statement 70

  4.1.2 Motivation 71

  4.2 Materials and Methods 72

  4.3 CCPS Design Challenges 74

  4.4 Cyber-Physical Systems Principles and Paradigms 77

  4.4.1 CCPS Conceptual Framework 79

  4.4.2 CCPS Modeling 81

  4.4.3 Other Modeling Issues in CCPS 82

  4.5 Conclusions and Future Enhancements 83

  4.5.1 Future Enhancements 83

  References 85

  5 Cognitive Cyber-Physical Security Challenges, Issues, and Recent Trends Over IoT 87
  Chinnaraj Govindasamy

  5.1 Introduction 88

  5.1.1 From IoT to CCPS-IoT 93

  5.1.2 Fundamental Cognitive Tasks 94

  5.2 Motivation and Challenges 94

  5.2.1 Motivation 94

  5.2.2 Challenges 95

  5.3 Security 96

  5.3.1 Physical Layer …