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Full Description
An authoritative and up-to-date discussion of securing future 6G networks
This book delivers a comprehensive, one-stop reference for 6G security, explaining the major technologies and use cases being contemplated for 6G, as well as the security issues relevant to those technologies and application areas. It explores security issues associated with key 6G technologies, including softwarization, disaggregation, slicing, blockchain, federated learning, non-terrestrial networks, and millimeter-wave, as well as major verticals enabled by 6G technology.
The book articulates critical technical challenges and opportunities for securing 6G systems, like how to secure autonomous vehicles with machine learning, and covers essential areas of a looming technological revolution that will pose significant security challenges for stakeholders.
Readers will also find:
A thorough introduction to the new frontiers opened up by 6G technology
Comprehensive explorations of securing radio access network slicing
Practical discussions on how to secure management with blockchain-based federated learning
Complete treatments of securing millimeter-wave communications and developing smart microgrids with distributed energy resources
Perfect for mobile network professionals and technologists, 6G Security: New Frontiers in Mobile Network Technologies and Verticals will also benefit service providers, researchers, and students with an interest in communications networks and their security.
Contents
Contents
Contributors
Foreword
Bibliography
Acknowledgments
Acronyms
1 Securing 6G: New Frontiers
1.1 Introduction
1.2 Generational evolution of networks and security mechanisms
1.3 What is coming in 6G?
1.3.1 Standardization Timeline
1.3.2 Key features of 6G
1.3.3 Open RAN
1.3.4 AI-native
1.4 The Road to Resilient 6G
Introduction of New Technologies
Multi-vendor Operation
Artificial Intelligence Everywhere
1.5 Technologies and Verticals
Bibliography
2 Securing Softwarization and Disaggregation
2.1 Introduction
2.2 Preliminaries
2.2.1 VNF & VNF Chaining
2.2.2 Security and Trust Management in 5G - State of the Art
2.2.3 Limitations of Centralized 5G Trust Management
2.3 Disaggregated Trust Management Framework
2.3.1 Trust Management for NFV
2.3.2 Creating Secure VNFs with Disaggregated Trust
2.3.3 Creating a Function that Maps VNF Chains to a 5G Network
2.3.4 Challenges and Open Problems
2.4 Securing O-RAN Systems
2.5 Key Takeaways
Bibliography
3 Securing Radio Access Network Slicing
3.1 Introduction
3.2 Network Slicing Using Machine Learning
3.2.1 RAN Slicing Resource Management
3.2.2 RAN Slicing Security
3.3 Adversarial Machine Learning
3.4 Adversarial Machine Learning for Wireless Systems
3.5 Attacks on Reinforcement Learning for RAN Slicing
3.6 Multitask Learning for RAN Slicing Security
3.7 Adversarial Attacks on Single-Task and Multitask Learning for RAN Slicing
3.8 Adversarial Training as the Defense against Adversarial Attacks on RAN Slicing
3.9 Key Takeaways
Bibliography
4 Securing Management with Blockchain-based Federated Learning
4.1 Introduction
4.1.1 Challenges and Contributions
4.2 Background
4.2.1 Blockchain
4.2.2 Federated Learning
4.2.3 Blockchain-empowered FL
4.2.4 Critical Limitations of Blockchain-based FL
4.3 BlockFed Framework
4.3.1 System Overview
4.3.2 Key Steps in BlockFed
4.4 Experimental Results
4.4.1 Experimental Setting
4.4.2 Performance Comparison
4.5 Key Takeaways
Bibliography
5 Securing Non-Terrestrial Networks
5.1 Introduction
5.1.1 Non-Terrestrial Network: The Definition
5.1.2 Motivation for the Non-Terrestrial Networks (NTN)
5.1.3 NTNs - A Historical Perspective and Emerging Industry Trends
5.1.4 3rd Generation Partnership Project (3GPP) NTN Roadmap
5.2 3GPP NTN Architecture
5.2.1 Types of NTN platforms
5.2.2 Types of Beams
5.2.3 NTN Architectures
5.3 Unique NTN Challenges and Associated Candidate Solutions
5.3.1 NTN-Specific Challenges
5.3.2 3GPP-defined NTN Solutions
5.4 Security Vulnerabilities in the NTN
5.4.1 Overview of NTN-Specific Interfaces
5.4.2 Analysis of Jamming Attacks on Wireless Interfaces of the NTN
5.4.3 Candidate Strategies for Enhanced NTN Security
5.5 Evolution of the NTN in 5G-Advanced and 6G
5.5.1 5G NTN Enhancements in 3GPP Release and beyond
5.5.2 Role of the NTN in 6G
5.6 Key Takeaways
Bibliography
6 Securing Next G Millimeter-wave and Terahertz Communication
6.1 Introduction
6.2 Security Vulnerabilities in High-Band Communication Systems
6.2.1 Pilot contamination attack
6.2.2 Beam sweeping attack
6.2.3 Attack against AI/ML-based beam management
6.3 PHY-Layer Authentication for High-Band Communication
6.3.1 Hardware-based RF Fingerprinting
6.3.2 Channel/Location-based Authentication
6.3.3 Hybrid RF Fingerprinting
6.4 Localization of Malicious High-Band Devices
6.4.1 BS to RIS AoD Estimation
6.4.2 ToF Estimation and Different RIS Beam Sweeping Capabilities
6.5 Securing AI/ML-Driven Beam Management
6.5.1 Adaptive Reduction of Beam Sweeping Opportunities
6.5.2 Hardening the AI/ML
6.6 Key Takeaways
Bibliography
7 Securing Cyber-physical Systems Using Homomorphic Encryption
7.1 Introduction
7.2 Background
7.2.1 Cyber-physical Systems
7.2.2 Fully Homomorphic Encryption
7.3 Standardization and Implementation of FHE
7.3.1 Standardization
7.3.2 Implementation
7.3.3 FHE Implementation in NextG Use Case: Healthcare
7.4 Recent Advancements in Securing CPS using FHE
7.4.1 Critical Infrastructures
7.4.2 Internet Of Things (IoT)
7.4.3 Biometric-based Authentication
7.4.4 Mobile Spatial Computing
7.5 Key Takeaways
Bibliography .
8 Securing Power Constrained Devices with Energy-Preserving Cryptography
8.1 Introduction
8.2 Task 1: Kinetic Energy Harvesting and Gait-based Node Authentication
8.2.1 Proposed Approach
8.2.2 Gait Signal
8.2.3 Experiments
8.3 Sub-threshold Computing for Cryptography
8.3.1 Proposed Approach
8.3.2 Experiments
8.4 Edge Based Re-configurable Cryptography
8.4.1 Background
8.4.2 Proposed Approach
8.4.3 Experiments
8.5 Key Takeaways
Bibliography
9 Securing Smart Microgrid with Distributed Energy Resources
9.1 Introduction
9.2 Microgrid: Overview
9.2.1 Microgrid Components
9.3 Energy Management System
9.3.1 Objective Function
9.3.2 Constraints
9.3.3 Simulation Model
9.3.4 Findings and Interpretation
9.4 Cybersecurity of Microgrids
9.4.1 Types of Cyber Threats
9.4.2 Detection of Security Threats
9.4.3 Adversarial Machine Learning Attack against Solar
PV Power Generation Forecasting
9.4.4 Findings and Interpretation
9.5 Key Takeaways
Bibliography
10. Securing Autonomous Vehicles with Machine Learning
10.1 Introduction
10.2 Machine learning for AVs
10.3 Adversarial Attacks against AVs
10.3.1 Overall Introduction
10.3.2 Adversarial Attack Categories
10.3.3 Adversarial Attacks
10.4 Performance Evaluation
10.4.1 Experimental Settings
10.4.2 Experimental Evaluations
10.5 Possible Defense Mechanisms
10.5.1 Approaches against Adversarial Attacks
10.5.2 Additional Measures for Securing ML for AVs in NextG
10.6 Key Takeaways
Bibliography
