ALI ISMAIL AWAD, PhD, is currently an Associate Professor with the College of Information Technology (CIT), United Arab Emirates University (UAEU), Al Ain, United Arab Emirates. He is also an Associate Professor with the Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden. He is an Associate Professor with the Electrical Engineering Department, Faculty of Engineering, Al-Azhar University at Qena, Qena, Egypt. He is also a Visiting Researcher at the University of Plymouth, United Kingdom. Dr. Awad is an Editorial Board Member of the Future Generation Computer Systems Journal, Computers & Security Journal, the Internet of Things, Engineering Cyber Physical Human Systems Journal, Health Information Science and Systems Journal, and IET Image Processing Journal. Dr. Awad is currently an IEEE senior member.JEMAL H. ABAWAJY, PhD, is a full professor at Faculty of Science, Engineering and Built Environment, Deakin University, Australia. He is a Senior Member of IEEE Society; IEEE Technical Committee on Scalable Computing (TCSC); IEEE Technical Committee on Dependable Computing and Fault Tolerance and IEEE Communication Society. His leadership is extensive spanning industrial, academic and professional areas (e.g., IEEE Technical Committee on Scalable Computing, Academic Board, Faculty Board and Research Integrity Advisory Group). Professor Abawajy is currently the Director of the Distributing System Security (DSS).

• About the Editors xiiiList of Contributors xvPreface xix1 Advanced Attacks and Protection Mechanisms in IoT Devices and Networks 1Lejla Batina, Nele Mentens, Markus Miettinen, Naila Mukhtar, Thien Duc Nguyen, Md Masoom Rabbani, Phillip Rieger, and Ahmad-Reza Sadeghi1.1 Introduction 21.2 Physical Security in IoT Devices 31.2.1 Physical Attacks 31.2.1.1 Side-channel Analysis Attacks 31.2.1.2 Fault Analysis Attacks 41.2.2 Profiling Attacks 41.2.3 Machine Learning and SCA 51.2.4 Real-world Attacks 71.2.5 Countermeasures 81.3 Remote Attestation in IoT Devices 91.3.1 Types of Remote Attestation 101.3.1.1 Software-based Remote Attestation 101.3.1.2 Hardware-based Remote Attestation 111.3.1.3 Hybrid Architecture-based Remote Attestation 121.3.2 Remote Attestation for Large IoT Networks 121.3.2.1 Classical Swarm Attestation Techniques 121.3.2.2 Swarm Attestation for IoT Services 141.3.3 Future Directions 141.3.3.1 Cloud-based RA Techniques 151.3.3.2 RA in Novel Internet Technologies 151.3.3.3 Blockchain Based RA 151.4 Intrusion Detection in IoT Networks 151.4.1 IoT Malware 151.4.2 Vulnerability Patching 161.4.3 Signature- and Anomaly Detection-based Network Intrusion Detection 171.4.4 Deep Learning-based Anomaly Detection 171.4.4.1 System Overview 181.4.4.2 Modeling Packet Sequence Patterns 181.4.4.3 Anomalous Packet Detection 191.4.5 Federated Deep Learning-based IoT Intrusion Detection System 201.4.5.1 Federated Learning 201.4.5.2 Federated Self-Learning Anomaly Detection 201.4.5.3 Challenges of Federated Learning-based Anomaly Detection System for IoT 22References 232 Human Aspects of IoT Security and Privacy 31Sune Von Solms and Steven Furnell2.1 Introduction 312.2 An Overview of the Domestic IoT Environment 332.3 Security Issues and the IoT Landscape 392.4 Human Factors Challenges in IoT Security and Privacy 412.4.1 Security Trade-offs for Individuals in IoT 432.4.2 Data Ownership and Use 442.4.3 Device Management and Administration Responsibilities 462.4.4 The Age of Unwanted Intelligence 472.5 Toward Improved User-facing Security in the IoT 492.6 Conclusion 51Acknowledgments 51References 513 Applying Zero Trust Security Principles to Defence Mechanisms Against Data Exfiltration Attacks 57Hugo Egerton, Mohammad Hammoudeh, Devrim Unal, and Bamidele Adebisi3.1 Introduction 573.2 Data Exfiltration Types, Attack Mechanisms, and Defence Techniques 593.2.1 Types of Data Exfiltration 593.2.1.1 Physical 593.2.1.2 Remote 603.2.2 Data Exfiltration Attack Techniques 603.2.2.1 Physical-based 603.2.2.2 Remote-based 613.2.3 Insider Data Exfiltration Threats 633.2.3.1 Types of Insider Threats 633.2.4 Approaches to Counter Data Exfiltration 653.2.4.1 Preventative 653.2.4.2 Detective 663.2.4.3 Investigative 663.2.5 Mechanisms to Defend Against Physical Data Exfiltration 673.2.5.1 Network-based 673.2.5.2 Physical-based 673.3 A Defence Mechanism for Physical Data Exfiltration Mitigation 683.3.1 Confidential Data Identification 683.3.2 Endpoint Access Rules 703.3.3 Data Fingerprinting 723.3.4 Relevance to Physical-Layer Protection 733.3.5 Complementing Existing Firewall and Application-based Measures 733.4 Implementation and Analysis 753.4.1 Experimental Setup 753.4.2 Threat Scenario 773.4.3 Scenario Execution and Analysis 773.5 Evaluation 803.5.1 Scenarios 813.5.2 Scenario 1 823.5.3 Scenario 2 823.5.4 Scenario 3 833.5.5 Results Analysis and Discussion 843.6 Conclusion 86References 864 eSIM-Based Authentication Protocol for UAV Remote Identification 91Abdulhadi Shoufan, Chan Yeob Yeun, and Bilal Taha4.1 Introduction 914.2 Drone Security 934.2.1 Drone Security in UTM 934.2.1.1 Physical Attacks 944.2.1.2 Cyber Attacks 944.2.1.3 Cyber-Physical Attacks 944.2.2 Security Attacks on Drones 954.2.3 Security Attacks from Drones 964.2.3.1 Spying and Surveillance 964.2.3.2 Smuggling 964.2.3.3 Physical Attacks 974.2.3.4 Cyber Attacks 974.3 Drone Safety 984.3.1 Drone Detection and Classification 984.3.2 Interdiction Technologies 984.4 UAV Remote Identification 1004.5 Authentication Protocol for Remote Identification 1034.5.1 Preliminaries 1044.5.1.1 Assumptions and Notations 1044.5.2 Registration 1064.5.3 Secure Communication Protocol 1074.5.3.1 M1: A Challenge from the Operator (A) to the Drone (B) 1084.5.3.2 M2: A Response from the Drone (B) to the Operator (A) 1094.5.3.3 M3: Control Message from the Operator to the Drone 1094.5.3.4 M4: Drone’s Response to the Control Message 1104.5.3.5 M5: Secure Broadcast of Remote Identification Message 1104.5.4 Security Analysis 1104.5.5 Formal Verification 1124.5.5.1 Declaration of User-Defined Types and Terms 1124.5.5.2 Declaration of Cryptographic Primitives 1134.5.5.3 Examples 1144.5.5.4 Reachability and Secrecy Checking 1144.5.5.5 Verifying Mutual Authentication 1154.6 Conclusion 117References 1175 Collaborative Intrusion Detection in the Era of IoT: Recent Advances and Challenges 123Wenjuan Li and Weizhi Meng5.1 Introduction 1235.2 Background 1245.2.1 Background on Intrusion Detection System 1245.2.2 Collaborative Intrusion Detection Framework 1265.3 Recent Development of Collaborative Intrusion Detection 1285.4 Open Challenges and Future Trend 1365.4.1 Advanced Insider Threats 1365.4.1.1 Advanced Attacks 1365.4.1.2 Solutions 1375.4.2 Open Challenges and Limitations 1385.4.3 Future Trend 1395.5 Conclusion 141References 1416 Cyber-Securing IoT Infrastructure by Modeling Network Traffic 151Hassan Habibi Gharakheili, Ayyoob Hamza, and Vijay Sivaraman6.1 Introduction 1516.2 Cyber-Attacks on IoT Infrastructure 1536.2.1 Eavesdropping 1546.2.1.1 Solutions 1546.2.2 Network Activity Analysis 1546.2.2.1 Solutions 1546.2.3 Active Reconnaissance 1556.2.3.1 Solutions 1556.2.4 Volumetric Attack 1566.2.4.1 Solutions 1566.2.5 Masquerading Attack 1576.2.5.1 Solutions 1576.2.6 Access Attack 1586.2.6.1 Solutions 1586.2.7 Active Crypto Attack 1586.2.7.1 Solutions 1596.2.8 Data Exfiltration 1596.2.8.1 Solutions 1596.2.9 Blocking Attack 1606.2.9.1 Solutions 1606.2.10 Sleep Deprivation Attack 1606.2.10.1 Solutions 1616.2.11 Trigger Action Attack 1616.2.11.1 Solutions 1616.3 Network Behavioral Model of IoTs 1626.3.1 Enforcing MUD Profile to Network 1626.3.2 MUD Protection Against Attacks 1646.3.2.1 To Internet 1666.3.2.2 From Internet 1666.3.2.3 From/To Local 1686.3.3 Monitoring MUD Activity 1696.4 Conclusion 170References 1707 Integrity of IoT Network Flow Records in Encrypted Traffic Analytics 177Aswani Kumar Cherukuri, Ikram Sumaiya Thaseen, Gang Li, Xiao Liu, Vinamra Das, and Aditya Raj7.1 Introduction 1777.2 Background 1807.2.1 Encrypted Traffic Analytics (ETA) 1807.2.2 Techniques for ETA 1817.2.3 Hashing for Flow Record Authentication 1827.3 Flow Based Telemetry 1837.3.1 Flow Metadata 1837.3.2 Flow-Based Approaches 1837.3.3 Threats on Flow Telemetry 1857.4 Hashing-Based MAC for Telemetry Data 1867.5 Experimental Analysis 1897.5.1 Hashed Flow Records 1897.5.2 Symmetric Encryption with Hashed Flow Records 1927.5.3 Asymmetric Encryption with Hashed Flow Records 1957.6 Conclusion 199List of Abbreviations 200Acknowledgment 200References 2008 Securing Contemporary eHealth Architectures: Techniques and Methods 207Naeem F. Syed, Zubair Baig, and Adnan Anwar8.1 Introduction 2078.2 eHealth 2088.2.1 Why IoT Is Important in eHealth? 2098.2.2 Fog or Edge Computing for eHealth 2108.2.3 Cloud Computing for eHealth 2128.2.4 Applications of IoT in eHealth 2128.2.4.1 Sleep Monitoring System 2128.2.4.2 Real Time and Advanced Health Diagnoses 2138.2.4.3 Emotion Detection 2138.2.4.4 Nutrition Monitoring System 2138.2.4.5 Detection of Dyslexia 2138.2.5 eHealth Security 2138.2.5.1 Implications of eHealth Security for Smart Cities 2148.3 eHealth Threat Landscape 2158.3.1 eHealth Threat Model 2158.3.1.1 eHealth Assets 2168.3.1.2 eHealth Attack Agents 2168.3.2 eHealth IoT Vulnerabilities and Threats 2188.3.2.1 Attacks in BAN 2188.3.2.2 Attacks in Communication Layer 2198.3.2.3 Attacks in Healthcare Provider Layer 2198.3.3 Real-world Attacks 2218.4 Countermeasures 2218.4.1 Patient Data Protection 2238.4.2 Device and Communication Security Measures 2248.4.2.1 Securing Communication 2258.4.3 Adaptive Security Framework 2268.4.4 Use Cases 2288.5 Conclusion 229References 2309 Security and Privacy of Smart Homes: Issues and Solutions 235Martin Lundgren and Ali Padyab9.1 Introduction 2359.2 State-of-the-Art in Smart Homes’ Security and Privacy 2369.2.1 Smart Home Technologies 2369.2.2 User-Centric Privacy 2379.2.3 Consequences of Data Breaches 2389.2.4 Dimensions of Privacy Concerns 2399.2.5 Consequences of Information Security 2409.2.6 A Framework for Security and Privacy Concerns 2429.3 Privacy Techniques and Mechanisms 2449.3.1 Cryptography 2449.3.2 Access Control 2469.3.3 Privacy Policy 2479.3.4 Anonymity 2489.3.5 UI/UX, User Awareness, and Control 2499.4 Toward Future Solutions 2509.5 Conclusion 251References 25210 IoT Hardware-Based Security: A Generalized Review of Threats and Countermeasures 261Catherine Higgins, Lucas McDonald, Muhammad Ijaz Ul Haq, and Saqib Hakak10.1 Introduction 26110.2 Hardware Attacks 26210.2.1 IoT Devices 26210.2.1.1 Node-Level Threats 26410.2.1.2 RFID Technology 26610.2.2 Hardware Design Threats 26910.2.2.1 Fake Replica 26910.2.2.2 Reverse Engineering 26910.2.2.3 Intellectual Property Hijacking 27010.2.2.4 Hardware Trojans 27010.2.3 Side-Channel Attacks 27010.2.3.1 Types of Side-Channel Attacks 27110.3 Physical Security Attacks Countermeasures 27410.3.1 Mitigation Techniques for IoT Hardware Attacks 27510.3.2 Side-Channel Attacks 27510.3.2.1 Hardware Trojans 27510.3.2.2 Power Analysis Attack 27910.3.2.3 Timing Attacks 28310.3.2.4 Electromagnetic Analysis Attacks 28410.3.2.5 Acoustic Crypto-Analysis Attack 28510.3.3 Integrated Circuits Security 28610.3.3.1 Countermeasures 28610.3.4 Radio Frequency Identification 28710.3.4.1 Physical Unclonable Function-based Authentication 28710.3.4.2 Preventing Physical Tampering Attacks (Enhancing Physical Security) 28710.3.4.3 Preventing Information Leakage 28810.3.4.4 Preventing Relay Attack 28810.4 Conclusion 289Acknowledgment 291References 291Index 297

ALI ISMAIL AWAD, PhD, is currently an Associate Professor with the College of Information Technology (CIT), United Arab Emirates University (UAEU), Al Ain, United Arab Emirates. He is also an Associate Professor with the Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden. He is an Associate Professor with the Electrical Engineering Department, Faculty of Engineering, Al-Azhar University at Qena, Qena, Egypt. He is also a Visiting Researcher at the University of Plymouth, United Kingdom. Dr. Awad is an Editorial Board Member of the Future Generation Computer Systems Journal, Computers & Security Journal, the Internet of Things, Engineering Cyber Physical Human Systems Journal, Health Information Science and Systems Journal, and IET Image Processing Journal. Dr. Awad is currently an IEEE senior member.JEMAL H. ABAWAJY, PhD, is a full professor at Faculty of Science, Engineering and Built Environment, Deakin University, Australia. He is a Senior Member of IEEE Society; IEEE Technical Committee on Scalable Computing (TCSC); IEEE Technical Committee on Dependable Computing and Fault Tolerance and IEEE Communication Society. His leadership is extensive spanning industrial, academic and professional areas (e.g., IEEE Technical Committee on Scalable Computing, Academic Board, Faculty Board and Research Integrity Advisory Group). Professor Abawajy is currently the Director of the Distributing System Security (DSS).

• About the Editors xiiiList of Contributors xvPreface xix1 Advanced Attacks and Protection Mechanisms in IoT Devices and Networks 1Lejla Batina, Nele Mentens, Markus Miettinen, Naila Mukhtar, Thien Duc Nguyen, Md Masoom Rabbani, Phillip Rieger, and Ahmad-Reza Sadeghi1.1 Introduction 21.2 Physical Security in IoT Devices 31.2.1 Physical Attacks 31.2.1.1 Side-channel Analysis Attacks 31.2.1.2 Fault Analysis Attacks 41.2.2 Profiling Attacks 41.2.3 Machine Learning and SCA 51.2.4 Real-world Attacks 71.2.5 Countermeasures 81.3 Remote Attestation in IoT Devices 91.3.1 Types of Remote Attestation 101.3.1.1 Software-based Remote Attestation 101.3.1.2 Hardware-based Remote Attestation 111.3.1.3 Hybrid Architecture-based Remote Attestation 121.3.2 Remote Attestation for Large IoT Networks 121.3.2.1 Classical Swarm Attestation Techniques 121.3.2.2 Swarm Attestation for IoT Services 141.3.3 Future Directions 141.3.3.1 Cloud-based RA Techniques 151.3.3.2 RA in Novel Internet Technologies 151.3.3.3 Blockchain Based RA 151.4 Intrusion Detection in IoT Networks 151.4.1 IoT Malware 151.4.2 Vulnerability Patching 161.4.3 Signature- and Anomaly Detection-based Network Intrusion Detection 171.4.4 Deep Learning-based Anomaly Detection 171.4.4.1 System Overview 181.4.4.2 Modeling Packet Sequence Patterns 181.4.4.3 Anomalous Packet Detection 191.4.5 Federated Deep Learning-based IoT Intrusion Detection System 201.4.5.1 Federated Learning 201.4.5.2 Federated Self-Learning Anomaly Detection 201.4.5.3 Challenges of Federated Learning-based Anomaly Detection System for IoT 22References 232 Human Aspects of IoT Security and Privacy 31Sune Von Solms and Steven Furnell2.1 Introduction 312.2 An Overview of the Domestic IoT Environment 332.3 Security Issues and the IoT Landscape 392.4 Human Factors Challenges in IoT Security and Privacy 412.4.1 Security Trade-offs for Individuals in IoT 432.4.2 Data Ownership and Use 442.4.3 Device Management and Administration Responsibilities 462.4.4 The Age of Unwanted Intelligence 472.5 Toward Improved User-facing Security in the IoT 492.6 Conclusion 51Acknowledgments 51References 513 Applying Zero Trust Security Principles to Defence Mechanisms Against Data Exfiltration Attacks 57Hugo Egerton, Mohammad Hammoudeh, Devrim Unal, and Bamidele Adebisi3.1 Introduction 573.2 Data Exfiltration Types, Attack Mechanisms, and Defence Techniques 593.2.1 Types of Data Exfiltration 593.2.1.1 Physical 593.2.1.2 Remote 603.2.2 Data Exfiltration Attack Techniques 603.2.2.1 Physical-based 603.2.2.2 Remote-based 613.2.3 Insider Data Exfiltration Threats 633.2.3.1 Types of Insider Threats 633.2.4 Approaches to Counter Data Exfiltration 653.2.4.1 Preventative 653.2.4.2 Detective 663.2.4.3 Investigative 663.2.5 Mechanisms to Defend Against Physical Data Exfiltration 673.2.5.1 Network-based 673.2.5.2 Physical-based 673.3 A Defence Mechanism for Physical Data Exfiltration Mitigation 683.3.1 Confidential Data Identification 683.3.2 Endpoint Access Rules 703.3.3 Data Fingerprinting 723.3.4 Relevance to Physical-Layer Protection 733.3.5 Complementing Existing Firewall and Application-based Measures 733.4 Implementation and Analysis 753.4.1 Experimental Setup 753.4.2 Threat Scenario 773.4.3 Scenario Execution and Analysis 773.5 Evaluation 803.5.1 Scenarios 813.5.2 Scenario 1 823.5.3 Scenario 2 823.5.4 Scenario 3 833.5.5 Results Analysis and Discussion 843.6 Conclusion 86References 864 eSIM-Based Authentication Protocol for UAV Remote Identification 91Abdulhadi Shoufan, Chan Yeob Yeun, and Bilal Taha4.1 Introduction 914.2 Drone Security 934.2.1 Drone Security in UTM 934.2.1.1 Physical Attacks 944.2.1.2 Cyber Attacks 944.2.1.3 Cyber-Physical Attacks 944.2.2 Security Attacks on Drones 954.2.3 Security Attacks from Drones 964.2.3.1 Spying and Surveillance 964.2.3.2 Smuggling 964.2.3.3 Physical Attacks 974.2.3.4 Cyber Attacks 974.3 Drone Safety 984.3.1 Drone Detection and Classification 984.3.2 Interdiction Technologies 984.4 UAV Remote Identification 1004.5 Authentication Protocol for Remote Identification 1034.5.1 Preliminaries 1044.5.1.1 Assumptions and Notations 1044.5.2 Registration 1064.5.3 Secure Communication Protocol 1074.5.3.1 M1: A Challenge from the Operator (A) to the Drone (B) 1084.5.3.2 M2: A Response from the Drone (B) to the Operator (A) 1094.5.3.3 M3: Control Message from the Operator to the Drone 1094.5.3.4 M4: Drone’s Response to the Control Message 1104.5.3.5 M5: Secure Broadcast of Remote Identification Message 1104.5.4 Security Analysis 1104.5.5 Formal Verification 1124.5.5.1 Declaration of User-Defined Types and Terms 1124.5.5.2 Declaration of Cryptographic Primitives 1134.5.5.3 Examples 1144.5.5.4 Reachability and Secrecy Checking 1144.5.5.5 Verifying Mutual Authentication 1154.6 Conclusion 117References 1175 Collaborative Intrusion Detection in the Era of IoT: Recent Advances and Challenges 123Wenjuan Li and Weizhi Meng5.1 Introduction 1235.2 Background 1245.2.1 Background on Intrusion Detection System 1245.2.2 Collaborative Intrusion Detection Framework 1265.3 Recent Development of Collaborative Intrusion Detection 1285.4 Open Challenges and Future Trend 1365.4.1 Advanced Insider Threats 1365.4.1.1 Advanced Attacks 1365.4.1.2 Solutions 1375.4.2 Open Challenges and Limitations 1385.4.3 Future Trend 1395.5 Conclusion 141References 1416 Cyber-Securing IoT Infrastructure by Modeling Network Traffic 151Hassan Habibi Gharakheili, Ayyoob Hamza, and Vijay Sivaraman6.1 Introduction 1516.2 Cyber-Attacks on IoT Infrastructure 1536.2.1 Eavesdropping 1546.2.1.1 Solutions 1546.2.2 Network Activity Analysis 1546.2.2.1 Solutions 1546.2.3 Active Reconnaissance 1556.2.3.1 Solutions 1556.2.4 Volumetric Attack 1566.2.4.1 Solutions 1566.2.5 Masquerading Attack 1576.2.5.1 Solutions 1576.2.6 Access Attack 1586.2.6.1 Solutions 1586.2.7 Active Crypto Attack 1586.2.7.1 Solutions 1596.2.8 Data Exfiltration 1596.2.8.1 Solutions 1596.2.9 Blocking Attack 1606.2.9.1 Solutions 1606.2.10 Sleep Deprivation Attack 1606.2.10.1 Solutions 1616.2.11 Trigger Action Attack 1616.2.11.1 Solutions 1616.3 Network Behavioral Model of IoTs 1626.3.1 Enforcing MUD Profile to Network 1626.3.2 MUD Protection Against Attacks 1646.3.2.1 To Internet 1666.3.2.2 From Internet 1666.3.2.3 From/To Local 1686.3.3 Monitoring MUD Activity 1696.4 Conclusion 170References 1707 Integrity of IoT Network Flow Records in Encrypted Traffic Analytics 177Aswani Kumar Cherukuri, Ikram Sumaiya Thaseen, Gang Li, Xiao Liu, Vinamra Das, and Aditya Raj7.1 Introduction 1777.2 Background 1807.2.1 Encrypted Traffic Analytics (ETA) 1807.2.2 Techniques for ETA 1817.2.3 Hashing for Flow Record Authentication 1827.3 Flow Based Telemetry 1837.3.1 Flow Metadata 1837.3.2 Flow-Based Approaches 1837.3.3 Threats on Flow Telemetry 1857.4 Hashing-Based MAC for Telemetry Data 1867.5 Experimental Analysis 1897.5.1 Hashed Flow Records 1897.5.2 Symmetric Encryption with Hashed Flow Records 1927.5.3 Asymmetric Encryption with Hashed Flow Records 1957.6 Conclusion 199List of Abbreviations 200Acknowledgment 200References 2008 Securing Contemporary eHealth Architectures: Techniques and Methods 207Naeem F. Syed, Zubair Baig, and Adnan Anwar8.1 Introduction 2078.2 eHealth 2088.2.1 Why IoT Is Important in eHealth? 2098.2.2 Fog or Edge Computing for eHealth 2108.2.3 Cloud Computing for eHealth 2128.2.4 Applications of IoT in eHealth 2128.2.4.1 Sleep Monitoring System 2128.2.4.2 Real Time and Advanced Health Diagnoses 2138.2.4.3 Emotion Detection 2138.2.4.4 Nutrition Monitoring System 2138.2.4.5 Detection of Dyslexia 2138.2.5 eHealth Security 2138.2.5.1 Implications of eHealth Security for Smart Cities 2148.3 eHealth Threat Landscape 2158.3.1 eHealth Threat Model 2158.3.1.1 eHealth Assets 2168.3.1.2 eHealth Attack Agents 2168.3.2 eHealth IoT Vulnerabilities and Threats 2188.3.2.1 Attacks in BAN 2188.3.2.2 Attacks in Communication Layer 2198.3.2.3 Attacks in Healthcare Provider Layer 2198.3.3 Real-world Attacks 2218.4 Countermeasures 2218.4.1 Patient Data Protection 2238.4.2 Device and Communication Security Measures 2248.4.2.1 Securing Communication 2258.4.3 Adaptive Security Framework 2268.4.4 Use Cases 2288.5 Conclusion 229References 2309 Security and Privacy of Smart Homes: Issues and Solutions 235Martin Lundgren and Ali Padyab9.1 Introduction 2359.2 State-of-the-Art in Smart Homes’ Security and Privacy 2369.2.1 Smart Home Technologies 2369.2.2 User-Centric Privacy 2379.2.3 Consequences of Data Breaches 2389.2.4 Dimensions of Privacy Concerns 2399.2.5 Consequences of Information Security 2409.2.6 A Framework for Security and Privacy Concerns 2429.3 Privacy Techniques and Mechanisms 2449.3.1 Cryptography 2449.3.2 Access Control 2469.3.3 Privacy Policy 2479.3.4 Anonymity 2489.3.5 UI/UX, User Awareness, and Control 2499.4 Toward Future Solutions 2509.5 Conclusion 251References 25210 IoT Hardware-Based Security: A Generalized Review of Threats and Countermeasures 261Catherine Higgins, Lucas McDonald, Muhammad Ijaz Ul Haq, and Saqib Hakak10.1 Introduction 26110.2 Hardware Attacks 26210.2.1 IoT Devices 26210.2.1.1 Node-Level Threats 26410.2.1.2 RFID Technology 26610.2.2 Hardware Design Threats 26910.2.2.1 Fake Replica 26910.2.2.2 Reverse Engineering 26910.2.2.3 Intellectual Property Hijacking 27010.2.2.4 Hardware Trojans 27010.2.3 Side-Channel Attacks 27010.2.3.1 Types of Side-Channel Attacks 27110.3 Physical Security Attacks Countermeasures 27410.3.1 Mitigation Techniques for IoT Hardware Attacks 27510.3.2 Side-Channel Attacks 27510.3.2.1 Hardware Trojans 27510.3.2.2 Power Analysis Attack 27910.3.2.3 Timing Attacks 28310.3.2.4 Electromagnetic Analysis Attacks 28410.3.2.5 Acoustic Crypto-Analysis Attack 28510.3.3 Integrated Circuits Security 28610.3.3.1 Countermeasures 28610.3.4 Radio Frequency Identification 28710.3.4.1 Physical Unclonable Function-based Authentication 28710.3.4.2 Preventing Physical Tampering Attacks (Enhancing Physical Security) 28710.3.4.3 Preventing Information Leakage 28810.3.4.4 Preventing Relay Attack 28810.4 Conclusion 289Acknowledgment 291References 291Index 297

ALI ISMAIL AWAD, PhD, is currently an Associate Professor with the College of Information Technology (CIT), United Arab Emirates University (UAEU), Al Ain, United Arab Emirates. He is also an Associate Professor with the Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden. He is an Associate Professor with the Electrical Engineering Department, Faculty of Engineering, Al-Azhar University at Qena, Qena, Egypt. He is also a Visiting Researcher at the University of Plymouth, United Kingdom. Dr. Awad is an Editorial Board Member of the Future Generation Computer Systems Journal, Computers & Security Journal, the Internet of Things, Engineering Cyber Physical Human Systems Journal, Health Information Science and Systems Journal, and IET Image Processing Journal. Dr. Awad is currently an IEEE senior member.JEMAL H. ABAWAJY, PhD, is a full professor at Faculty of Science, Engineering and Built Environment, Deakin University, Australia. He is a Senior Member of IEEE Society; IEEE Technical Committee on Scalable Computing (TCSC); IEEE Technical Committee on Dependable Computing and Fault Tolerance and IEEE Communication Society. His leadership is extensive spanning industrial, academic and professional areas (e.g., IEEE Technical Committee on Scalable Computing, Academic Board, Faculty Board and Research Integrity Advisory Group). Professor Abawajy is currently the Director of the Distributing System Security (DSS).

• About the Editors xiiiList of Contributors xvPreface xix1 Advanced Attacks and Protection Mechanisms in IoT Devices and Networks 1Lejla Batina, Nele Mentens, Markus Miettinen, Naila Mukhtar, Thien Duc Nguyen, Md Masoom Rabbani, Phillip Rieger, and Ahmad-Reza Sadeghi1.1 Introduction 21.2 Physical Security in IoT Devices 31.2.1 Physical Attacks 31.2.1.1 Side-channel Analysis Attacks 31.2.1.2 Fault Analysis Attacks 41.2.2 Profiling Attacks 41.2.3 Machine Learning and SCA 51.2.4 Real-world Attacks 71.2.5 Countermeasures 81.3 Remote Attestation in IoT Devices 91.3.1 Types of Remote Attestation 101.3.1.1 Software-based Remote Attestation 101.3.1.2 Hardware-based Remote Attestation 111.3.1.3 Hybrid Architecture-based Remote Attestation 121.3.2 Remote Attestation for Large IoT Networks 121.3.2.1 Classical Swarm Attestation Techniques 121.3.2.2 Swarm Attestation for IoT Services 141.3.3 Future Directions 141.3.3.1 Cloud-based RA Techniques 151.3.3.2 RA in Novel Internet Technologies 151.3.3.3 Blockchain Based RA 151.4 Intrusion Detection in IoT Networks 151.4.1 IoT Malware 151.4.2 Vulnerability Patching 161.4.3 Signature- and Anomaly Detection-based Network Intrusion Detection 171.4.4 Deep Learning-based Anomaly Detection 171.4.4.1 System Overview 181.4.4.2 Modeling Packet Sequence Patterns 181.4.4.3 Anomalous Packet Detection 191.4.5 Federated Deep Learning-based IoT Intrusion Detection System 201.4.5.1 Federated Learning 201.4.5.2 Federated Self-Learning Anomaly Detection 201.4.5.3 Challenges of Federated Learning-based Anomaly Detection System for IoT 22References 232 Human Aspects of IoT Security and Privacy 31Sune Von Solms and Steven Furnell2.1 Introduction 312.2 An Overview of the Domestic IoT Environment 332.3 Security Issues and the IoT Landscape 392.4 Human Factors Challenges in IoT Security and Privacy 412.4.1 Security Trade-offs for Individuals in IoT 432.4.2 Data Ownership and Use 442.4.3 Device Management and Administration Responsibilities 462.4.4 The Age of Unwanted Intelligence 472.5 Toward Improved User-facing Security in the IoT 492.6 Conclusion 51Acknowledgments 51References 513 Applying Zero Trust Security Principles to Defence Mechanisms Against Data Exfiltration Attacks 57Hugo Egerton, Mohammad Hammoudeh, Devrim Unal, and Bamidele Adebisi3.1 Introduction 573.2 Data Exfiltration Types, Attack Mechanisms, and Defence Techniques 593.2.1 Types of Data Exfiltration 593.2.1.1 Physical 593.2.1.2 Remote 603.2.2 Data Exfiltration Attack Techniques 603.2.2.1 Physical-based 603.2.2.2 Remote-based 613.2.3 Insider Data Exfiltration Threats 633.2.3.1 Types of Insider Threats 633.2.4 Approaches to Counter Data Exfiltration 653.2.4.1 Preventative 653.2.4.2 Detective 663.2.4.3 Investigative 663.2.5 Mechanisms to Defend Against Physical Data Exfiltration 673.2.5.1 Network-based 673.2.5.2 Physical-based 673.3 A Defence Mechanism for Physical Data Exfiltration Mitigation 683.3.1 Confidential Data Identification 683.3.2 Endpoint Access Rules 703.3.3 Data Fingerprinting 723.3.4 Relevance to Physical-Layer Protection 733.3.5 Complementing Existing Firewall and Application-based Measures 733.4 Implementation and Analysis 753.4.1 Experimental Setup 753.4.2 Threat Scenario 773.4.3 Scenario Execution and Analysis 773.5 Evaluation 803.5.1 Scenarios 813.5.2 Scenario 1 823.5.3 Scenario 2 823.5.4 Scenario 3 833.5.5 Results Analysis and Discussion 843.6 Conclusion 86References 864 eSIM-Based Authentication Protocol for UAV Remote Identification 91Abdulhadi Shoufan, Chan Yeob Yeun, and Bilal Taha4.1 Introduction 914.2 Drone Security 934.2.1 Drone Security in UTM 934.2.1.1 Physical Attacks 944.2.1.2 Cyber Attacks 944.2.1.3 Cyber-Physical Attacks 944.2.2 Security Attacks on Drones 954.2.3 Security Attacks from Drones 964.2.3.1 Spying and Surveillance 964.2.3.2 Smuggling 964.2.3.3 Physical Attacks 974.2.3.4 Cyber Attacks 974.3 Drone Safety 984.3.1 Drone Detection and Classification 984.3.2 Interdiction Technologies 984.4 UAV Remote Identification 1004.5 Authentication Protocol for Remote Identification 1034.5.1 Preliminaries 1044.5.1.1 Assumptions and Notations 1044.5.2 Registration 1064.5.3 Secure Communication Protocol 1074.5.3.1 M1: A Challenge from the Operator (A) to the Drone (B) 1084.5.3.2 M2: A Response from the Drone (B) to the Operator (A) 1094.5.3.3 M3: Control Message from the Operator to the Drone 1094.5.3.4 M4: Drone’s Response to the Control Message 1104.5.3.5 M5: Secure Broadcast of Remote Identification Message 1104.5.4 Security Analysis 1104.5.5 Formal Verification 1124.5.5.1 Declaration of User-Defined Types and Terms 1124.5.5.2 Declaration of Cryptographic Primitives 1134.5.5.3 Examples 1144.5.5.4 Reachability and Secrecy Checking 1144.5.5.5 Verifying Mutual Authentication 1154.6 Conclusion 117References 1175 Collaborative Intrusion Detection in the Era of IoT: Recent Advances and Challenges 123Wenjuan Li and Weizhi Meng5.1 Introduction 1235.2 Background 1245.2.1 Background on Intrusion Detection System 1245.2.2 Collaborative Intrusion Detection Framework 1265.3 Recent Development of Collaborative Intrusion Detection 1285.4 Open Challenges and Future Trend 1365.4.1 Advanced Insider Threats 1365.4.1.1 Advanced Attacks 1365.4.1.2 Solutions 1375.4.2 Open Challenges and Limitations 1385.4.3 Future Trend 1395.5 Conclusion 141References 1416 Cyber-Securing IoT Infrastructure by Modeling Network Traffic 151Hassan Habibi Gharakheili, Ayyoob Hamza, and Vijay Sivaraman6.1 Introduction 1516.2 Cyber-Attacks on IoT Infrastructure 1536.2.1 Eavesdropping 1546.2.1.1 Solutions 1546.2.2 Network Activity Analysis 1546.2.2.1 Solutions 1546.2.3 Active Reconnaissance 1556.2.3.1 Solutions 1556.2.4 Volumetric Attack 1566.2.4.1 Solutions 1566.2.5 Masquerading Attack 1576.2.5.1 Solutions 1576.2.6 Access Attack 1586.2.6.1 Solutions 1586.2.7 Active Crypto Attack 1586.2.7.1 Solutions 1596.2.8 Data Exfiltration 1596.2.8.1 Solutions 1596.2.9 Blocking Attack 1606.2.9.1 Solutions 1606.2.10 Sleep Deprivation Attack 1606.2.10.1 Solutions 1616.2.11 Trigger Action Attack 1616.2.11.1 Solutions 1616.3 Network Behavioral Model of IoTs 1626.3.1 Enforcing MUD Profile to Network 1626.3.2 MUD Protection Against Attacks 1646.3.2.1 To Internet 1666.3.2.2 From Internet 1666.3.2.3 From/To Local 1686.3.3 Monitoring MUD Activity 1696.4 Conclusion 170References 1707 Integrity of IoT Network Flow Records in Encrypted Traffic Analytics 177Aswani Kumar Cherukuri, Ikram Sumaiya Thaseen, Gang Li, Xiao Liu, Vinamra Das, and Aditya Raj7.1 Introduction 1777.2 Background 1807.2.1 Encrypted Traffic Analytics (ETA) 1807.2.2 Techniques for ETA 1817.2.3 Hashing for Flow Record Authentication 1827.3 Flow Based Telemetry 1837.3.1 Flow Metadata 1837.3.2 Flow-Based Approaches 1837.3.3 Threats on Flow Telemetry 1857.4 Hashing-Based MAC for Telemetry Data 1867.5 Experimental Analysis 1897.5.1 Hashed Flow Records 1897.5.2 Symmetric Encryption with Hashed Flow Records 1927.5.3 Asymmetric Encryption with Hashed Flow Records 1957.6 Conclusion 199List of Abbreviations 200Acknowledgment 200References 2008 Securing Contemporary eHealth Architectures: Techniques and Methods 207Naeem F. Syed, Zubair Baig, and Adnan Anwar8.1 Introduction 2078.2 eHealth 2088.2.1 Why IoT Is Important in eHealth? 2098.2.2 Fog or Edge Computing for eHealth 2108.2.3 Cloud Computing for eHealth 2128.2.4 Applications of IoT in eHealth 2128.2.4.1 Sleep Monitoring System 2128.2.4.2 Real Time and Advanced Health Diagnoses 2138.2.4.3 Emotion Detection 2138.2.4.4 Nutrition Monitoring System 2138.2.4.5 Detection of Dyslexia 2138.2.5 eHealth Security 2138.2.5.1 Implications of eHealth Security for Smart Cities 2148.3 eHealth Threat Landscape 2158.3.1 eHealth Threat Model 2158.3.1.1 eHealth Assets 2168.3.1.2 eHealth Attack Agents 2168.3.2 eHealth IoT Vulnerabilities and Threats 2188.3.2.1 Attacks in BAN 2188.3.2.2 Attacks in Communication Layer 2198.3.2.3 Attacks in Healthcare Provider Layer 2198.3.3 Real-world Attacks 2218.4 Countermeasures 2218.4.1 Patient Data Protection 2238.4.2 Device and Communication Security Measures 2248.4.2.1 Securing Communication 2258.4.3 Adaptive Security Framework 2268.4.4 Use Cases 2288.5 Conclusion 229References 2309 Security and Privacy of Smart Homes: Issues and Solutions 235Martin Lundgren and Ali Padyab9.1 Introduction 2359.2 State-of-the-Art in Smart Homes’ Security and Privacy 2369.2.1 Smart Home Technologies 2369.2.2 User-Centric Privacy 2379.2.3 Consequences of Data Breaches 2389.2.4 Dimensions of Privacy Concerns 2399.2.5 Consequences of Information Security 2409.2.6 A Framework for Security and Privacy Concerns 2429.3 Privacy Techniques and Mechanisms 2449.3.1 Cryptography 2449.3.2 Access Control 2469.3.3 Privacy Policy 2479.3.4 Anonymity 2489.3.5 UI/UX, User Awareness, and Control 2499.4 Toward Future Solutions 2509.5 Conclusion 251References 25210 IoT Hardware-Based Security: A Generalized Review of Threats and Countermeasures 261Catherine Higgins, Lucas McDonald, Muhammad Ijaz Ul Haq, and Saqib Hakak10.1 Introduction 26110.2 Hardware Attacks 26210.2.1 IoT Devices 26210.2.1.1 Node-Level Threats 26410.2.1.2 RFID Technology 26610.2.2 Hardware Design Threats 26910.2.2.1 Fake Replica 26910.2.2.2 Reverse Engineering 26910.2.2.3 Intellectual Property Hijacking 27010.2.2.4 Hardware Trojans 27010.2.3 Side-Channel Attacks 27010.2.3.1 Types of Side-Channel Attacks 27110.3 Physical Security Attacks Countermeasures 27410.3.1 Mitigation Techniques for IoT Hardware Attacks 27510.3.2 Side-Channel Attacks 27510.3.2.1 Hardware Trojans 27510.3.2.2 Power Analysis Attack 27910.3.2.3 Timing Attacks 28310.3.2.4 Electromagnetic Analysis Attacks 28410.3.2.5 Acoustic Crypto-Analysis Attack 28510.3.3 Integrated Circuits Security 28610.3.3.1 Countermeasures 28610.3.4 Radio Frequency Identification 28710.3.4.1 Physical Unclonable Function-based Authentication 28710.3.4.2 Preventing Physical Tampering Attacks (Enhancing Physical Security) 28710.3.4.3 Preventing Information Leakage 28810.3.4.4 Preventing Relay Attack 28810.4 Conclusion 289Acknowledgment 291References 291Index 297