Security and Privacy for 6G Massive IoT

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.

• List of Contributors xiiiAcknowledgements xixIntroduction xxi1 Threat Landscape for 6G-Enabled Massive IoT 1Maria Papaioannou, Georgios Mantas, Firooz B. Saghezchi, Georgios Kambourakis, Felipe Gil-Castiñeira, Raúl Santos de la Cámara, and Jonathan Rodriguez1.1 Introduction 11.2 6G Vision and Core Values 21.3 Emerging Massive IoT Applications Enabled by 6G 31.3.1 Enabling Sustainability 41.3.1.1 E-health for All 51.3.1.2 Institutional Coverage 61.3.1.3 Earth Monitor 61.3.1.4 Autonomous Supply Chains 61.3.1.5 Sustainable Food Production 71.3.1.6 Network Trade-Offs for Minimized Environmental Impact 71.3.1.7 Network Functionality for Crisis Resilience 81.3.2 Massive Twinning 81.3.2.1 Digital Twins for Manufacturing 91.3.2.2 Immersive Smart Cities 101.3.2.3 Internet of Tags 111.3.3 Telepresence 121.3.3.1 Fully Merged Cyber–Physical Worlds 121.3.3.2 Mixed Reality Co-design 141.3.3.3 Immersive Sport Event 141.3.3.4 Merged Reality Game/Work 151.3.4 From Robots to Cobots 151.3.4.1 Consumer Robots 151.3.4.2 AI Partners 161.3.4.3 Interacting and Cooperative Mobile Robots 161.3.4.4 Flexible Manufacturing 171.3.4.5 Situation-Aware Device Reconfiguration 171.3.5 Trusted Embedded Networks 181.3.5.1 Human-Centric Communications 181.3.5.2 Infrastructure-less Network Extensions and Embedded Networks 191.3.5.3 Local Coverage for Temporary Usage 201.3.5.4 Small Coverage, Low-Power Micro-Network in Networks for Production and Manufacturing 201.3.6 Hyperconnected Resilient Network Infrastructures 201.3.6.1 Sensor Infrastructure Web 211.3.6.2 AI-Assisted Vehicle-to-Everything (V2X) 211.3.6.3 Interconnected IoT Micro-Networks 221.3.6.4 Enhanced Public Protection 231.4 Overview of a 6G Network Architecture to Enable Massive IoT 231.4.1 Space Network 241.4.2 Air Network 251.4.3 Ground Network 251.4.4 Sea/Underwater Network 261.4.4.1 Surface Network 261.4.4.2 Underwater Network 261.5 Security Objectives in Massive IoT in 6G 261.5.1 Confidentiality 261.5.2 Integrity 271.5.3 Availability 271.5.4 Authentication 281.5.5 Authorization 281.6 Security Threats in Massive IoT in 6G 281.6.1 Security Threats to Data Confidentiality 291.6.2 Security Threats to Data Integrity 291.6.3 Security Threats to Availability 301.6.4 Security Threats to Authentication 311.6.5 Security Threats to Authorization 311.7 Conclusion 32References 332 Secure Edge Intelligence in the 6G Era 35Tanesh Kumar, Juha Partala, Tri Nguyen, Lalita Agrawal, Ayan Mondal, Abhishek Kumar, Ijaz Ahmad, Ella Peltonen, Susanna Pirttikangas, and Erkki Harjula2.1 Introduction 352.2 Background 362.2.1 Edge Computing and Its Importance 362.2.2 Emergence of Edge Intelligence 382.2.3 Fusion of Edge Intelligence and 6G 392.2.4 The Need for Secure Edge Intelligence 402.3 Security Challenges in 6G EI 402.3.1 Computational Offloading 402.3.2 Security of Machine Learning 412.3.3 Post-quantum Cryptography 422.4 Privacy Challenges in 6G EI 422.5 Trust Challenges in 6G EI 442.6 Security Standardization for EI and 6G 462.7 Conclusion 47References 473 Privacy-Preserving Machine Learning for Massive IoT Deployments 53Najwa Aaraj, Abdelrahaman Aly, Alvaro Garcia-Banda, Chiara Marcolla, Victor Sucasas, and Ajith Suresh3.1 Introduction 533.2 PPML for IoT 543.3 Secure Multiparty Computation 563.3.1 MPC: Security Models and Setups 603.3.2 MPC: Privacy and Output Guarantees 613.3.3 MPC: Arithmetic 613.3.4 MPC: Preprocessing 633.4 Fully Homomorphic Encryption 633.4.1 FHE: Bottlenecks and Advantages 643.4.2 Comparison with MPC 653.4.3 Generations of FHE Schemes 663.5 Oblivious Neural Networks (ONN) 673.5.1 Two-Party (2PC) 673.5.2 Two-Party with Helper (2PC +) 673.5.3 Three-Party (3PC) and Four-Party (4PC) 693.5.4 n-Party (MPC) 693.5.5 A Closer Look at PPML Frameworks 693.5.5.1 CryptoNets 693.5.5.2 SecureML 703.5.5.3 MiniONN 703.5.5.4 DeepSecure 723.5.5.5 Gazelle 733.5.5.6 Delphi 743.5.5.7 Aby 3 743.5.5.8 Fantastic Four 773.5.5.9 Crypten 773.5.5.10 Fanng–mpc 783.6 Decision Trees 783.6.1 Structure of a Decision Tree 793.6.2 Training 793.6.3 Computational Setups 803.6.4 Inference 813.6.4.1 The Three Stages Model 813.6.5 Relevant Contributions 833.6.5.1 On Passive Security 833.6.5.2 On Active Security 843.7 Software and Frameworks 853.8 Lightweight FHE and MPC for IoT 863.8.1 MPC Outsourcing 863.8.2 Hybrid-FHE 873.9 Alternative Solutions 903.10 Conclusions 90References 914 Federated Learning-Based Intrusion Detection Systems for Massive IoT 101Filippos Pelekoudas-Oikonomou, Parya H. Mirzaee, Waleed Hathal, Georgios Mantas, Jonathan Rodriguez, Haitham Cruickshank, and Zhili Sun4.1 Introduction 1014.2 Intrusion Detection Systems (IDSs) in IoT 1034.2.1 Fundamentals of IDSs 1034.2.2 Machine Learning Techniques for IDSs in IoT 1044.2.3 Limitations of ML-Based IDSs in IoT 1064.3 Federated Learning: A Decentralized ML Approach 1074.3.1 Definition of Federated Learning 1074.3.2 Categories of Federated Learning 1084.3.3 Federated Learning Architectures 1084.3.3.1 Horizontal Federated Learning (HFL) 1104.3.3.2 Vertical Federated Learning (VFL) 1114.3.3.3 Federated Transfer Learning (FTL) 1114.4 Federated Learning-Based IDSs for IoT 1134.4.1 FL-Based IDSs in IoT Use Cases 1134.4.1.1 FL-Based IDSs in Smart Homes 1134.4.1.2 FL-Based IDSs in Industrial IoT 1144.4.1.3 FL-Based IDSs in Agricultural IoT 1154.4.1.4 FL-Based IDS in Vehicular IoT Networks 1164.4.2 Cross-layer FL for Lightweight IoT Privacy-Preserving IDS 1164.5 Model Aggregation Approaches and Algorithms in FL 1174.5.1 Model Aggregation Approaches in FL 1174.5.2 Model Aggregation Algorithms in FL 1194.6 Challenges and Future Directions in FL-Based IDS for IoT 1214.6.1 Validation and Standardization 1214.6.2 Data Heterogeneity and Non-IID Data 1214.6.3 Security and Privacy Enhancements 1214.6.4 Communication Efficiency and Scalability 1224.6.5 Explainability and Interpretability 1224.7 Conclusion 122List of Abbreviations 123References 1245 Securing Massive IoT Using Network Slicing and Blockchain 129Shihan Bao, Zhili Sun, and Haitham Cruickshank5.1 Introduction 1295.2 Background 1315.2.1 Massive IoT 1315.2.2 Blockchain 1325.2.2.1 Blockchain Applications 1325.2.2.2 Consensus Mechanisms 1335.3 Challenges on Massive IoT 1355.3.1 Security Requirements in Massive IoT 1355.3.2 Privacy Requirements in Massive IoT 1365.3.3 Location Privacy Challenges 1385.4 Securing IoT Using Network Slicing and Blockchain 1385.4.1 Network Slicing Standardization 1385.4.2 Network Slicing Security, Privacy, and Trust Threats for 5G and Beyond 1425.4.3 Blockchain-Enabled Secure Network Slicing 1445.4.3.1 Integration of Distributed Ledger Technology (DLT) with Network Slicing 1445.4.3.2 Blockchain-Powered Network Slicing in Verticals 1445.4.3.3 Multiple Participant Coordination and Trust Management by Blockchain for Network Slicing 1455.5 Open Challenges 1465.5.1 Edge Intelligence in Network Slicing 1475.5.2 Post-quantum Security 1475.5.3 Blockchain Scalability 1475.5.4 RAN Slicing 1485.6 Conclusion 148References 1486 Physical Layer Security for RF-Based Massive IoT 155Marcus de Ree, Seda Dogan-Tusha, Elmehdi Illi, Marwa Qaraqe, Saud Althunibat, Georgios Mantas, and Jonathan Rodriguez6.1 Introduction 1556.2 Physical Layer-Based Key Establishment 1566.2.1 Introduction 1566.2.2 Channel Reciprocity-Based Key Establishment 1576.2.2.1 Principles and Assumptions 1576.2.2.2 Evaluation Metrics 1596.2.2.3 Key Establishment Model 1596.2.3 Signal Source Indistinguishability-Based Key Establishment 1616.2.3.1 Principles and Assumptions 1616.2.3.2 Evaluation Metrics 1626.2.3.3 Key Establishment Model 1626.2.3.4 Security and Performance Evaluation 1636.2.4 Final Remarks 1646.3 Physical Layer-Based Node Authentication 1646.3.1 Introduction 1646.3.2 Key-Based Physical Layer Authentication 1656.3.2.1 Tag-Embedding Authentication 1666.3.3 Channel-Based Keyless Physical Layer Authentication 1666.3.3.1 RSS-Based Authentication 1676.3.3.2 Frequency-Based Authentication 1676.3.3.3 CIR-Based Authentication 1676.3.3.4 Pilot-Based Authentication 1686.3.3.5 Machine Learning-Based Authentication 1686.3.4 Device-Based Keyless Physical Layer Authentication 1686.3.4.1 Radio Frequency Fingerprinting 1686.3.4.2 Physically Unclonable Function 1696.3.5 Final Remarks 1706.4 Physical Layer-Based Data Confidentiality 1716.4.1 Introduction 1716.4.2 Key-Based Data Confidentiality – Physical Layer Encryption 1716.4.2.1 Channel Coding-Based Physical Layer Encryption 1726.4.2.2 Signal Modulation-Based Physical Layer Encryption 1726.4.3 Keyless Data Confidentiality 1736.4.3.1 Security Metrics 1746.4.3.2 Channel Coding-Based Data Confidentiality 1746.4.3.3 Artificial Noise-Based Extensions 1756.4.3.4 Directional Modulation-Based Extensions 1766.4.4 Final Remarks 1766.5 Physical Layer-Based Detection of Malicious Nodes 1776.5.1 Introduction 1776.5.2 Data Injection Attack Detection 1776.5.3 Sybil Attack Detection 1786.5.4 Sleep Deprivation Attack Detection 1796.5.5 Final Remarks 1806.6 Conclusion 180List of Abbreviations 181References 1827 Quantum Security for the Tactile Internet 193Shima Hassanpour, Riccardo Bassoli, Janis Nötzel, Frank H. P. Fitzek, Holger Boche, and Thorsten Strufe7.1 Introduction 1937.2 Preliminaries – Quantum Mechanics 1977.2.1 Finite Dimensional Quantum Systems 1977.2.2 Quantum Gates 1977.2.3 Quantum Measurement 1987.2.4 Fock Space 1997.2.5 Continuous Variable Quantum States 1997.2.6 Gaussian States 2007.2.7 Conjugate Coding 2017.2.8 Quantum Entanglement 2017.2.9 Entanglement Swapping 2027.3 Preliminaries – Security 2037.3.1 Formal Cryptographic Frameworks 2047.3.2 Practical Source and Security 2047.4 Quantum Key Distribution 2057.4.1 Discrete Variable Protocols 2057.4.1.1 The Example of BB 84 2077.4.1.2 Security Argument 2097.4.2 Continuous Variable Protocols 2117.5 Integrated Physical Layer Security 2117.5.1 Conventional Approaches 2127.5.2 Quantum Approaches 2137.5.2.1 Notation 2137.5.2.2 Quantum Physical Layer Security 2147.6 Challenges and Known Attacks on Quantum Security 2147.7 Oblivious Transfer (OT) 2177.7.1 Probabilistic Formulation of OT 2187.7.2 Relevance of OT 2197.7.3 Bit Commitment 2197.7.4 Why Bit Commitment Is Important to Oblivious Transfer 2197.8 Conclusion 220Acknowledgment 221References 2218 Physical Layer Security for Terahertz Communications in Massive IoT 229Vitaly Petrov, Hichem Guerboukha, Zhambyl Shaikhanov, Edward W. Knightly, Daniel M. Mittleman, and Josep M. Jornet8.1 Introduction 2298.2 Information Theoretic Analysis of Eavesdropping 2308.3 Eavesdropping THz Links 2328.3.1 Security of Directional THz Links 2328.3.2 Metasurface-in-the-Middle Attack 2338.4 Multi-path THz Communications 2368.4.1 Multi-path THz Communications to Improve Security Against Eavesdropping 2368.4.2 Evaluation Framework 2378.4.3 Security vs. Capacity Trade-Off 2398.5 Absolute Security 2418.6 Jamming THz Links 2438.7 Summary and the Road Ahead 246References 246Index 251