Security and Privacy for 6G Massive IoT
Description
Anticipate the security and privacy threats of the future with this groundbreaking text
The development of the next generation of mobile networks (6G), which is expected to be widely deployed by 2030, promises to revolutionize the Internet of Things (IoT), interconnecting a massive number of IoT devices (massive IoT) on a scale never before envisioned. These devices will enable the operation of a wide spectrum of massive IoT applications such as immersive smart cities, autonomous supply chain, flexible manufacturing and more. However, the vast number of interconnected IoT devices in the emerging massive IoT applications will make them vulnerable to an unprecedented variety of security and privacy threats, which must be anticipated in order to harness the transformative potential of these technologies.
Security and Privacy for 6G Massive IoT addresses this new and expanding threat landscape and the challenges it poses for network security companies and professionals. It offers a unique and comprehensive understanding of these threats, their likely manifestations, and the solutions available to counter them. The result creates a foundation for future efforts to research and develop further solutions based on essential 6G technologies.
Readers will also find:
- Analysis based on the four-tier network architecture of 6G, enhanced by Edge Computing and Edge Intelligence
- Detailed coverage of 6G enabling technologies including blockchain, distributed machine learning, and many more
- Scenarios, use cases, and security and privacy requirements for 6G Massive IoT applications
Security and Privacy for 6G Massive IoT is ideal for research engineers working in the area of IoT security and designers working on new 6G security products, among many others.
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Persons
Georgios Mantas, PhD, is a Researcher at the Instituto de Telecomunicações - Aveiro, Portugal, and a part-time Senior Lecturer in Digital Security & Management with the Faculty of Engineering and Science at the University of Greenwich, Chatham Maritime, UK. He is a member of the IEEE, and has published widely on IoT security and privacy subjects.
Firooz B. Saghezchi, PhD, is a Senior Researcher at the Chair for Distributed Signal Processing of RWTH Aachen University, Aachen, Germany. He is a Senior Member of the IEEE and has published widely on wireless communications and cybersecurity subjects.
Jonathan Rodriguez, PhD, DSc, is Professor of Mobile and Satellite Communications in the Faculty of Computing, Engineering, and Science at the University of South Wales, Pontypridd, UK. His career-long contribution and impact to mobile communications and security research have led to his DSc award in 2022.
Victor Sucasas, PhD, is a Senior Director in the Cryptography Research Center at the Technology Innovation Institute (TII), Abu Dhabi, UAE. He leads the Confidential Computing Team, covering privacy enhancing technologies. He is a Senior IEEE and ComSoc member, and an EAI fellow, and has published widely on security and privacy issues.
Content
List of Contributors xiii
Acknowledgements xix
Introduction xxi
1 Threat Landscape for 6G-Enabled Massive IoT 1
Maria Papaioannou, Georgios Mantas, Firooz B. Saghezchi, Georgios Kambourakis, Felipe Gil-Castiñeira, Raúl Santos de la Cámara, and Jonathan Rodriguez
1.1 Introduction 1
1.2 6G Vision and Core Values 2
1.3 Emerging Massive IoT Applications Enabled by 6G 3
1.3.1 Enabling Sustainability 4
1.3.1.1 E-health for All 5
1.3.1.2 Institutional Coverage 6
1.3.1.3 Earth Monitor 6
1.3.1.4 Autonomous Supply Chains 6
1.3.1.5 Sustainable Food Production 7
1.3.1.6 Network Trade-Offs for Minimized Environmental Impact 7
1.3.1.7 Network Functionality for Crisis Resilience 8
1.3.2 Massive Twinning 8
1.3.2.1 Digital Twins for Manufacturing 9
1.3.2.2 Immersive Smart Cities 10
1.3.2.3 Internet of Tags 11
1.3.3 Telepresence 12
1.3.3.1 Fully Merged Cyber-Physical Worlds 12
1.3.3.2 Mixed Reality Co-design 14
1.3.3.3 Immersive Sport Event 14
1.3.3.4 Merged Reality Game/Work 15
1.3.4 From Robots to Cobots 15
1.3.4.1 Consumer Robots 15
1.3.4.2 AI Partners 16
1.3.4.3 Interacting and Cooperative Mobile Robots 16
1.3.4.4 Flexible Manufacturing 17
1.3.4.5 Situation-Aware Device Reconfiguration 17
1.3.5 Trusted Embedded Networks 18
1.3.5.1 Human-Centric Communications 18
1.3.5.2 Infrastructure-less Network Extensions and Embedded Networks 19
1.3.5.3 Local Coverage for Temporary Usage 20
1.3.5.4 Small Coverage, Low-Power Micro-Network in Networks for Production and Manufacturing 20
1.3.6 Hyperconnected Resilient Network Infrastructures 20
1.3.6.1 Sensor Infrastructure Web 21
1.3.6.2 AI-Assisted Vehicle-to-Everything (V2X) 21
1.3.6.3 Interconnected IoT Micro-Networks 22
1.3.6.4 Enhanced Public Protection 23
1.4 Overview of a 6G Network Architecture to Enable Massive IoT 23
1.4.1 Space Network 24
1.4.2 Air Network 25
1.4.3 Ground Network 25
1.4.4 Sea/Underwater Network 26
1.4.4.1 Surface Network 26
1.4.4.2 Underwater Network 26
1.5 Security Objectives in Massive IoT in 6G 26
1.5.1 Confidentiality 26
1.5.2 Integrity 27
1.5.3 Availability 27
1.5.4 Authentication 28
1.5.5 Authorization 28
1.6 Security Threats in Massive IoT in 6G 28
1.6.1 Security Threats to Data Confidentiality 29
1.6.2 Security Threats to Data Integrity 29
1.6.3 Security Threats to Availability 30
1.6.4 Security Threats to Authentication 31
1.6.5 Security Threats to Authorization 31
1.7 Conclusion 32
References 33
2 Secure Edge Intelligence in the 6G Era 35
Tanesh Kumar, Juha Partala, Tri Nguyen, Lalita Agrawal, Ayan Mondal, Abhishek Kumar, Ijaz Ahmad, Ella Peltonen, Susanna Pirttikangas, and Erkki Harjula
2.1 Introduction 35
2.2 Background 36
2.2.1 Edge Computing and Its Importance 36
2.2.2 Emergence of Edge Intelligence 38
2.2.3 Fusion of Edge Intelligence and 6G 39
2.2.4 The Need for Secure Edge Intelligence 40
2.3 Security Challenges in 6G EI 40
2.3.1 Computational Offloading 40
2.3.2 Security of Machine Learning 41
2.3.3 Post-quantum Cryptography 42
2.4 Privacy Challenges in 6G EI 42
2.5 Trust Challenges in 6G EI 44
2.6 Security Standardization for EI and 6G 46
2.7 Conclusion 47
References 47
3 Privacy-Preserving Machine Learning for Massive IoT Deployments 53
Najwa Aaraj, Abdelrahaman Aly, Alvaro Garcia-Banda, Chiara Marcolla, Victor Sucasas, and Ajith Suresh
3.1 Introduction 53
3.2 PPML for IoT 54
3.3 Secure Multiparty Computation 56
3.3.1 MPC: Security Models and Setups 60
3.3.2 MPC: Privacy and Output Guarantees 61
3.3.3 MPC: Arithmetic 61
3.3.4 MPC: Preprocessing 63
3.4 Fully Homomorphic Encryption 63
3.4.1 FHE: Bottlenecks and Advantages 64
3.4.2 Comparison with MPC 65
3.4.3 Generations of FHE Schemes 66
3.5 Oblivious Neural Networks (ONN) 67
3.5.1 Two-Party (2PC) 67
3.5.2 Two-Party with Helper (2PC +) 67
3.5.3 Three-Party (3PC) and Four-Party (4PC) 69
3.5.4 n-Party (MPC) 69
3.5.5 A Closer Look at PPML Frameworks 69
3.5.5.1 CryptoNets 69
3.5.5.2 SecureML 70
3.5.5.3 MiniONN 70
3.5.5.4 DeepSecure 72
3.5.5.5 Gazelle 73
3.5.5.6 Delphi 74
3.5.5.7 Aby 3 74
3.5.5.8 Fantastic Four 77
3.5.5.9 Crypten 77
3.5.5.10 Fanng-mpc 78
3.6 Decision Trees 78
3.6.1 Structure of a Decision Tree 79
3.6.2 Training 79
3.6.3 Computational Setups 80
3.6.4 Inference 81
3.6.4.1 The Three Stages Model 81
3.6.5 Relevant Contributions 83
3.6.5.1 On Passive Security 83
3.6.5.2 On Active Security 84
3.7 Software and Frameworks 85
3.8 Lightweight FHE and MPC for IoT 86
3.8.1 MPC Outsourcing 86
3.8.2 Hybrid-FHE 87
3.9 Alternative Solutions 90
3.10 Conclusions 90
References 91
4 Federated Learning-Based Intrusion Detection Systems for Massive IoT 101
Filippos Pelekoudas-Oikonomou, Parya H. Mirzaee, Waleed Hathal, Georgios Mantas, Jonathan Rodriguez, Haitham Cruickshank, and Zhili Sun
4.1 Introduction 101
4.2 Intrusion Detection Systems (IDSs) in IoT 103
4.2.1 Fundamentals of IDSs 103
4.2.2 Machine Learning Techniques for IDSs in IoT 104
4.2.3 Limitations of ML-Based IDSs in IoT 106
4.3 Federated Learning: A Decentralized ML Approach 107
4.3.1 Definition of Federated Learning 107
4.3.2 Categories of Federated Learning 108
4.3.3 Federated Learning Architectures 108
4.3.3.1 Horizontal Federated Learning (HFL) 110
4.3.3.2 Vertical Federated Learning (VFL) 111
4.3.3.3 Federated Transfer Learning (FTL) 111
4.4 Federated Learning-Based IDSs for IoT 113
4.4.1 FL-Based IDSs in IoT Use Cases 113
4.4.1.1 FL-Based IDSs in Smart Homes 113
4.4.1.2 FL-Based IDSs in Industrial IoT 114
4.4.1.3 FL-Based IDSs in Agricultural IoT 115
4.4.1.4 FL-Based IDS in Vehicular IoT Networks 116
4.4.2 Cross-layer FL for Lightweight IoT Privacy-Preserving IDS 116
4.5 Model Aggregation Approaches and Algorithms in FL 117
4.5.1 Model Aggregation Approaches in FL 117
4.5.2 Model Aggregation Algorithms in FL 119
4.6 Challenges and Future Directions in FL-Based IDS for IoT 121
4.6.1 Validation and Standardization 121
4.6.2 Data Heterogeneity and Non-IID Data 121
4.6.3 Security and Privacy Enhancements 121
4.6.4 Communication Efficiency and Scalability 122
4.6.5 Explainability and Interpretability 122
4.7 Conclusion 122
List of Abbreviations 123
References 124
5 Securing Massive IoT Using Network Slicing and Blockchain 129
Shihan Bao, Zhili Sun, and Haitham Cruickshank
5.1 Introduction 129
5.2 Background 131
5.2.1 Massive IoT 131
5.2.2 Blockchain 132
5.2.2.1 Blockchain Applications 132
5.2.2.2 Consensus Mechanisms 133
5.3 Challenges on Massive IoT 135
5.3.1 Security Requirements in Massive IoT 135
5.3.2 Privacy Requirements in Massive IoT 136
5.3.3 Location Privacy Challenges 138
5.4 Securing IoT Using Network Slicing and Blockchain 138
5.4.1 Network Slicing Standardization 138
5.4.2 Network Slicing Security, Privacy, and Trust Threats for 5G and Beyond 142
5.4.3 Blockchain-Enabled Secure Network Slicing 144
5.4.3.1 Integration of Distributed Ledger Technology (DLT) with Network Slicing 144
5.4.3.2 Blockchain-Powered Network Slicing in Verticals 144
5.4.3.3 Multiple Participant Coordination and Trust Management by Blockchain for Network Slicing 145
5.5 Open Challenges 146
5.5.1 Edge Intelligence in Network Slicing 147
5.5.2 Post-quantum Security 147
5.5.3 Blockchain Scalability 147
5.5.4 RAN Slicing 148
5.6 Conclusion 148
References 148
6 Physical Layer Security for RF-Based Massive IoT 155
Marcus de Ree, Seda Dogan-Tusha, Elmehdi Illi, Marwa Qaraqe, Saud Althunibat, Georgios Mantas, and Jonathan Rodriguez
6.1 Introduction 155
6.2 Physical Layer-Based Key Establishment 156
6.2.1 Introduction 156
6.2.2 Channel Reciprocity-Based Key Establishment 157
6.2.2.1 Principles and Assumptions 157
6.2.2.2 Evaluation Metrics 159
6.2.2.3 Key Establishment Model 159
6.2.3 Signal Source Indistinguishability-Based Key Establishment 161
6.2.3.1 Principles and Assumptions 161
6.2.3.2 Evaluation Metrics 162
6.2.3.3 Key Establishment Model 162
6.2.3.4 Security and Performance Evaluation 163
6.2.4 Final Remarks 164
6.3 Physical Layer-Based Node Authentication 164
6.3.1 Introduction 164
6.3.2 Key-Based Physical Layer Authentication 165
6.3.2.1 Tag-Embedding Authentication 166
6.3.3 Channel-Based Keyless Physical Layer Authentication 166
6.3.3.1 RSS-Based Authentication 167
6.3.3.2 Frequency-Based Authentication 167
6.3.3.3 CIR-Based Authentication 167
6.3.3.4 Pilot-Based Authentication 168
6.3.3.5 Machine Learning-Based Authentication 168
6.3.4 Device-Based Keyless Physical Layer Authentication 168
6.3.4.1 Radio Frequency Fingerprinting 168
6.3.4.2 Physically Unclonable Function 169
6.3.5 Final Remarks 170
6.4 Physical Layer-Based Data Confidentiality 171
6.4.1 Introduction 171
6.4.2 Key-Based Data Confidentiality - Physical Layer Encryption 171
6.4.2.1 Channel Coding-Based Physical Layer Encryption 172
6.4.2.2 Signal Modulation-Based Physical Layer Encryption 172
6.4.3 Keyless Data Confidentiality 173
6.4.3.1 Security Metrics 174
6.4.3.2 Channel Coding-Based Data Confidentiality 174
6.4.3.3 Artificial Noise-Based Extensions 175
6.4.3.4 Directional Modulation-Based Extensions 176
6.4.4 Final Remarks 176
6.5 Physical Layer-Based Detection of Malicious Nodes 177
6.5.1 Introduction 177
6.5.2 Data Injection Attack Detection 177
6.5.3 Sybil Attack Detection 178
6.5.4 Sleep Deprivation Attack Detection 179
6.5.5 Final Remarks 180
6.6 Conclusion 180
List of Abbreviations 181
References 182
7 Quantum Security for the Tactile Internet 193
Shima Hassanpour, Riccardo Bassoli, Janis Nötzel, Frank H. P. Fitzek, Holger Boche, and Thorsten Strufe
7.1 Introduction 193
7.2 Preliminaries - Quantum Mechanics 197
7.2.1 Finite Dimensional Quantum Systems 197
7.2.2 Quantum Gates 197
7.2.3 Quantum Measurement 198
7.2.4 Fock Space 199
7.2.5 Continuous Variable Quantum States 199
7.2.6 Gaussian States 200
7.2.7 Conjugate Coding 201
7.2.8 Quantum Entanglement 201
7.2.9 Entanglement Swapping 202
7.3 Preliminaries - Security 203
7.3.1 Formal Cryptographic Frameworks 204
7.3.2 Practical Source and Security 204
7.4 Quantum Key Distribution 205
7.4.1 Discrete Variable Protocols 205
7.4.1.1 The Example of BB 84 207
7.4.1.2 Security Argument 209
7.4.2 Continuous Variable Protocols 211
7.5 Integrated Physical Layer Security 211
7.5.1 Conventional Approaches 212
7.5.2 Quantum Approaches 213
7.5.2.1 Notation 213
7.5.2.2 Quantum Physical Layer Security 214
7.6 Challenges and Known Attacks on Quantum Security 214
7.7 Oblivious Transfer (OT) 217
7.7.1 Probabilistic Formulation of OT 218
7.7.2 Relevance of OT 219
7.7.3 Bit Commitment 219
7.7.4 Why Bit Commitment Is Important to Oblivious Transfer 219
7.8 Conclusion 220
Acknowledgment 221
References 221
8 Physical Layer Security for Terahertz Communications in Massive IoT 229
Vitaly Petrov, Hichem Guerboukha, Zhambyl Shaikhanov, Edward W. Knightly, Daniel M. Mittleman, and Josep M. Jornet
8.1 Introduction 229
8.2 Information Theoretic Analysis of Eavesdropping 230
8.3 Eavesdropping THz Links 232
8.3.1 Security of Directional THz Links 232
8.3.2 Metasurface-in-the-Middle Attack 233
8.4 Multi-path THz Communications 236
8.4.1 Multi-path THz Communications to Improve Security Against Eavesdropping 236
8.4.2 Evaluation Framework 237
8.4.3 Security vs. Capacity Trade-Off 239
8.5 Absolute Security 241
8.6 Jamming THz Links 243
8.7 Summary and the Road Ahead 246
References 246
Index 251
