Secure and Trusted Cyber Physical Systems: Recent Approaches and Future Directions

Description

This book highlights the latest design and development of security issues and various defences to construct safe, secure and trusted Cyber-Physical Systems (CPS). In addition, the book presents a detailed analysis of the recent approaches to security solutions and future research directions for large-scale CPS, including its various challenges and significant security requirements. Furthermore, the book provides practical guidance on delivering robust, privacy, and trust-aware CPS at scale. Finally, the book presents a holistic insight into IoT technologies, particularly its latest development in strategic applications in mission-critical systems, including large-scale Industrial IoT, Industry 4.0, and Industrial Control Systems. As such, the book offers an essential reference guide about the latest design and development in CPS for students, engineers, designers, and professional developers. Preface Contents Challenges and Opportunities of Blockchain for Cyber Threat Intelligence Sharing 1 Introduction 2 Overview of Blockchain and CTI 2.1 Blockchain 2.2 CTI 3 Blockchain-Based CTI Sharing 4 Challenges 4.1 Producer Consumer Imbalance 4.2 Legal and Regulatory Obligations 4.3 Data Validity 4.4 Intelligent Intelligence 4.5 Privacy, Trust, and Accountability 5 Opportunities 5.1 Incentivised Sharing 5.2 Deposits 5.3 Reputational System 5.4 Access Control 5.5 Intelligence Mining 6 Related Work and Discussion 7 Conclusion References System Identification Methods for Industrial Control Systems 1 Introduction 2 Classification of Industrial Control Systems 2.1 System Dynamics 2.2 System Architecture 2.3 Design Implementation 2.4 Automation 3 Model Discovery from the System Dynamics Perspective 3.1 Time-Driven Approach 3.2 Event-Driven Approach 3.3 Hybrid System Identification 4 Model Discovery from the System Architecture Perspective 5 Model Discovery from the Design Perspective 6 Model Discovery from the Automation Perspective 7 Conclusion References Vulnerability Management in IIoT-Based Systems: What, Why and How 1 Introduction 2 Vulnerability Management 2.1 Challenges of IIoT-Based Systems for VM 3 Tools and Techniques for Systematic VM 3.1 Vulnerability Discovery 3.2 Vulnerability Analysis 3.3 Vulnerability Prioritization 3.4 Vulnerability Remediation 3.5 Vulnerability Verification and Monitoring 4 Reseach Directions 5 Conclusion References Review of Cyber Security for Power Trading and Communication Systems 1 Introduction 2 Theme 1: Security Technology 3 Theme 2: Security Technology for Protecting Energy Trading and Auditing Systems 4 Theme 3: Communication Technology and Cloud Computing 5 Theme 4: Network Performance and Security Management for Communication System 6 Conclusion References DDoS Threats and Solutions for 5G-Enabled IoT Networks 1 Introduction 2 DDoS Attack Mitigation Categories 2.1 Classification of DDoS Attack 2.2 Attack Prevention 2.3 DDoS Attack Detection 2.4 DDOS Attack Mitigation Schemes 3 5G/IoT Security Solutions Against DDoS Attack 3.1 DDoS Attack Prevention for 5G/IoT 3.2 DDoS Attack Detection for 5G/IoT 3.3 DDoS Attack Mitigation for 5G/IoT 3.4 Hybrid Approach Against DDoS for 5G/IoT 4 Conclusion References A Lightweight Blockchain-Based Trust Management Framework for Access Control in IoT 1 Introduction 1.1 Overview of IoT 1.2 Overview of an Access Control Mechanism 1.3 Overview of Blockchain 1.4 Limitations of Blockchain 1.5 Contributions of the Chapter 2 Background and Preliminaries 2.1 The Security Concerns in IoT 2.2 The Traditional Approach: Access Control 2.3 Blockchain 2.4 Blockchain Integration with Access Control 2.5 Blockchain Integration with Trust Management 2.6 Trust-Based Consensus Algorithm 2.7 Security Attacks in Blockchain 3 Blockchain-Based Trust Management Framework 3.1 Overview 3.2 Smart Contract Systems 3.3 Transactions and Blocks 3.4 Reputation-Based Consensus Algorithm 3.5 Reputation-Based Verification Mechanism 3.6 Summary 4 Framework Analysis 4.1 Validity 4.2 Agreement 4.3 Liveness 4.4 Scalability 4.5 Fairness 4.6 Security 5 Experiment 5.1 Delay Time 5.2 Verification Time 5.3 Transactions Per Second 5.4 Sybil Attack Evaluation 5.5 DDoS Attack Evaluation 6 Conclusion References Utilising K-Means Clustering and Naive Bayes for IoT Anomaly Detection: A Hybrid Approach 1 Introduction 1.1 Background 1.2 Motivation of the Research 1.3 Chapter Overview 2 Related Work 2.1 IoT Overview 2.2 Contrasting IoT and Standard Networking Data 2.3 Anomalies 2.4 Anomalies Within This Chapter 2.5 Anomaly Detection 2.6 Machine Learning 2.7 Supervised Learning 2.8 Unsupervised Learning 2.9 Data Clustering 2.10 Partional Clustering 3 Proposed Algorithm 3.1 KMANB 3.2 Algorithm Design 4 Evaluation 4.1 Methodology 4.2 Train and Test Dataset 4.3 Processed Dataset 4.4 Hypothesis 4.5 Evaluation Metrics 5 Results 5.1 Train and Test Results 5.2 Train and Test with no Highest Ranked Feature 5.3 Processed Dataset Results 5.4 Processed Dataset no Highest Ranked Feature 6 Discussion 6.1 Accuracy, Precision and Recall Scores 6.2 Train and Test Times 6.3 Algorithm Optimisation 6.4 Scalability of KMANB 6.5 Traditional IoT AD Algorithms Versus the KMANB 7 Conclusion and Future Work References