Security and Privacy Schemes for Dense 6G Wireless Communication Networks

Cover
Contents
About the editors
Acknowledgments
Preface
1 Introduction to emerging security and privacy schemes for dense 6G wireless communication networks
Abstract
1.1 Introduction
1.1.1 Key contributions
1.1.2 Chapter organization
1.2 Related work
1.3 6G security and privacy
1.3.1 Automated management system
1.3.2 Virtualization security solution
1.3.3 Data security using AI
1.3.4 Post quantum cryptography (PQC)
1.3.5 Preserving user privacy
1.4 6G security and privacy challenges
1.4.1 UAV/satellite communication
1.4.2 Molecular communication (MC)
1.4.3 Terahertz communication (THzCom)
1.4.4 Visible light communication (VLC)
1.4.5 Blockchain/distributed ledger technology
1.4.6 RIS
1.4.7 Ambient backscatter communication (AmBC)
1.4.8 Cell-free massive MIMO (CF-mMIMO) communication
1.4.9 QC
1.4.10 Internet of BioNanoThings (IoBNT)
1.4.11 Internet of NanoThings (IoNT)
1.4.12 Pervasive AI
1.5 Addressing 6G security and privacy challenges
1.5.1 PLS schemes
1.5.2 Distributed AI/ML schemes
1.5.3 Quantum cryptography schemes
1.5.4 Blockchain-based security schemes
1.5.5 Other security schemes
1.6 Lessons learned
1.7 Conclusion and recommendations
Acknowledgment
References
2 History of security and privacy in wireless communication systems: open research issues and future directions
Abstract
2.1 Introduction
2.2 History and evolution of wireless communication
2.3 General security issues
2.3.1 Physical layer attacks
2.3.2 MAC layer attacks
2.3.3 Network layer attacks
2.3.4 Transport layer attacks
2.3.5 Application layer attacks
2.4 Security and privacy in wireless communication
2.4.1 1G
2.4.2 2G – GSM
2.4.3 3G – UMTS
2.4.4 4G – LTE
2.4.5 5G
2.5 Emerging wireless communication systems
2.5.1 Low-cost IoT devices
2.5.2 Ultra-reliable and low latency communications (URLLC)
2.5.3 eMBB
2.5.4 Massive machine-type communication (mMTC)
2.6 Application of AI and ML to wireless security system design
2.7 Security issues and challenges in future wireless communication systems
2.7.1 AI
2.7.2 Molecular communication (MC)
2.7.3 Quantum communication (QC)
2.7.4 Blockchain
2.7.5 Terahertz (THz) technology
2.7.6 Visible light communication (VLC)
2.8 Conclusions and recommendations
References
3 Artificial intelligence-enabled security systems for 6G wireless networks: algorithms, strategies, and applications
Abstract
3.1 Introduction
3.1.1 Contribution
3.1.2 Chapter organization
3.2 Overview of 6G technology
3.2.1 6G technology requirements
3.3 The security and privacy issues with 6G wireless communication and prospective attacks
3.4 AI-based security and privacy for 6G wireless communication technology
3.5 The future directions of AI-based security and privacy for 6G wireless communication technology
3.6 Conclusion and future directions
Acknowledgment
References
4 The vision of 6G security and privacy
Abstract
4.1 Introduction
4.1.1 Why the migration from 5G to 6G
4.1.2 Carrier aggregation
4.1.3 Security
4.1.4 Heterogeneity
4.1.5 Latency of links
4.1.6 Network availability
4.1.7 Scalability and communication speed
4.1.8 Link reliability
4.2 Review of emerging issues in 6G
4.2.1 Quantum communication issue
4.2.2 Molecular communication issue
4.2.3 Visible light communication
4.2.4 Distributed ledger technology issue
4.2.5 Flexible radio access limits
4.2.6 Heterogeneous high-frequency band (HHFB)
4.2.7 Tactile communication
4.3 Evolution of security and privacy schemes in wireless systems: 1G to 5G
4.3.1 1G network
4.3.2 2G network
4.3.3 3G network
4.3.4 4G network
4.3.5 5G network
4.4 Technical overview of 6G network
4.4.1 Intelligent reflecting surface
4.4.2 AI
4.4.3 Cell-free mMIMO
4.4.4 Edge intelligence
4.4.5 Holographic beamforming
4.4.6 Terahertz communication
4.5 Security concerns in 6G
4.5.1 An overview of 6G specification
4.6 6G architecture
4.6.1 Intelligent radio
4.6.2 Real-time intelligent edge (RTIE)
4.6.3 Intelligence network management
4.6.4 The 6G threat landscape
4.6.5 Legacy design security (pre-6G)
4.6.6 AI-related security challenges
4.7 Threat mitigation and countermeasures
4.7.1 Poisonous attacks on ML systems
4.7.2 Evasion attacks
4.7.3 ML API-based attacks
4.7.4 Infrastructure physical attacks
4.7.5 Compromise of AI framework
4.8 Recent trends and future directions
4.8.1 Recent trends
4.8.2 Future directions
4.9 Conclusion
Acknowledgment
References
5 Security threat landscape for 6G architecture
Abstract
5.1 Introduction
5.2 Designing 6G wireless systems with reconfigurable intelligent surfaces
5.3 PLS for 6G systems
5.4 The related works considering performance analysis of RIS-NOMA
5.5 A case study: PLS for RIS-NOMA
5.5.1 System model
5.5.2 Secrecy outage probability analysis
5.6 Numerical results and discussions
5.7 Conclusion
References
6 Dynamic optical beam transmitter of secure visible light communication systems
Abstract
6.1 Introduction
6.2 Optical beams characteristics
6.2.1 Lambertian optical beams
6.2.2 Non-Lambertian optical beams
6.3 The static and dynamic optical beam transmitter
6.3.1 Static optical beam transmitter
6.3.2 Dynamic optical beam transmitter
6.4 Numerical evaluation
6.5 Conclusion
Funding
References
7 A new machine learning-based scheme for physical layer security
Abstract
7.1 Introduction
7.2 System model
7.3 Proposed machine learning algorithm for detecting the presence of an active Eve
7.3.1 DNN-based scheme
7.3.2 SVM-based scheme
7.3.3 NB-based scheme
7.4 Simulation results and discussion
7.5 Conclusion
References
8 Vehicular ad hoc networks employing intelligent reflective surfaces for physical layer security
Abstract
8.1 Introduction
8.2 Related works
8.3 PLS through smart IRS
8.3.1 IRS-SR for PLS
8.3.2 IRS-AP for PLS
8.4 Discussions on simulations
8.5 Conclusions
Acknowledgment
References
9 Physical layer security solutions and technologies
Abstract
9.1 Introduction
9.1.1 Shannon cryptosystem
9.1.2 Computational security and its limitations
9.1.3 The physical layer security concept
9.1.4 Chapter organization
9.2 Fundamentals of physical layer security
9.2.1 The wiretap channel
9.2.2 Secrecy capacity
9.2.3 Wiretap codes
9.3 Physical layer security approaches
9.3.1 Extracting secret keys at the physical layer
9.3.2 Jamming and beamforming in multiple antenna systems
9.3.3 Cooperative jamming
9.4 Enabling physical layer security in 5G and beyond
9.4.1 Multilayer security approach
9.4.2 Wiretap codes for 5G-NR
9.4.3 Symmetric encryption with PHY key generation
9.4.4 Extending CoMP to cooperative jamming
9.5 Conclusion
References
10 Steganography-based secure communication via single carrier frequency division multiple access (SC-FDMA) transceiver
Abstract
10.1 Introduction
10.1.1 Related works
10.1.2 Security
10.1.3 Multiple access scheme
10.1.4 OFDM
10.1.5 SC-FDMA
10.1.6 Least significant bit (LSB) algorithm
10.1.7 Modified LSB algorithm
10.2 Proposed methodology
10.3 Performance metrics
10.3.1 Mean square error (MSE)
10.3.2 Peak signal-to-noise ratio (PSNR)
10.3.3 Structural Similarity Index (SSIM)
10.3.4 Average difference (AD)
10.3.5 Normalized cross-correlation (NCC)
10.3.6 Normalized absolute error (NAE)
10.3.7 Maximum difference (MD)
10.4 Results and discussion
10.5 Conclusion and future scope
References
11 A lightweight algorithm for the detection of fake incident reports in wireless communication systems
Abstract
11.1 Introduction
11.2 Related work
11.3 Assumptions
11.3.1 Sensor networks
11.3.2 Attack model
11.4 Proposed method
11.4.1 Overview
11.4.2 Processes
11.4.3 Update of tokens and Bloom filters
11.5 Analysis
11.5.1 Hop counts are required until the devices identify fake incident reports
11.5.2 The amount of traffic generated per class in an attack
11.5.3 The amount of communication generated by correct incident reports
11.5.4 Energy consumption
11.6 Evaluation
11.6.1 Parameter selection
11.6.2 Evaluation results
11.7 Discussion
11.8 Conclusion
Acknowledgment
References
12 A real-time intrusion detection system for service availability in cloud computing environments
Abstract
12.1 Introduction
12.1.1 Key contributions of the chapter
12.1.2 Chapter organization
12.2 Related work
12.3 Theoretical background of security issues in cloud computing
12.3.1 Cyber attacks
12.3.2 DDoS in cloud computing
12.3.3 IDS
12.3.4 Anomaly-based IDS
12.3.5 ML in security
12.3.6 Ensemble learning
12.3.7 Dataset description
12.4 Research methodology
12.4.1 Preprocessing
12.4.2 Model development
12.4.3 KNN
12.4.4 Logistic regression
12.4.5 Decision tree
12.4.6 Multi-layer perceptron
12.5 Results and discussions
12.6 Conclusions and future scope
References
13 Addressing the security challenges of IoT-enabled networks using artificial intelligence, machine learning, and blockchain techn
Abstract
13.1 Introduction
13.1.1 Objective
13.1.2 Chapter organization
13.2 Related work
13.3 IoT architecture, protocol, applications for 6G networks
13.3.1 IoT infrastructure
13.3.2 Standard protocols
13.3.3 Applications of IoT-enabled 6G networks
13.3.4 Key areas of 6G networks
13.4 Attacks in IoT-enabled 6G systems
13.5 Analysis of security challenges and issues in 6G networks
13.5.1 Using ML techniques for 6G-enabled IoT security issues
13.5.2 Using AI techniques for 6G-enabled IoT security issues
13.5.3 Using blockchain technology for 6G-enabled IoT security issues
13.6 Summary of the review
13.6.1 Critical analysis of ML, AI and blockchain technology
13.7 Conclusion and future scope
References
14 Alleviating 6G security and privacy issues using artificial intelligence
Abstract
14.1 Introduction
14.1.1 Contributions
14.1.2 Chapter organisation
14.2 Related works
14.2.1 Summary of related works
14.3 Addressing 6G security and privacy issues using AI/ML
14.3.1 The role of AI in 6G security
14.3.2 The role of AI on 6G privacy
14.3.3 Challenges with security and confidentiality in 6G technologies
14.4 Solutions to 6G security and privacy challenges
14.5 Application of blockchain technology in alleviating security and privacy in 6G networks
14.6 Network optimisation in 6G network
14.6.1 Problem formulations and method
14.6.2 Power distribution and joint channel allocation for downlink and uplink in a system
14.6.3 Numerical simulation results
14.7 Lessons learned
14.7.1 Lessons learned from earlier wireless generations (1G–5G)
14.7.2 Future directions
14.8 Conclusions
References
15 Interference and phase noise in millimeter wave MIMO-NOMA and OFDM systems for beyond 5G networks
Abstract
15.1 Introduction
15.1.1 Key contributions of the chapter
15.1.2 Chapter organization
15.2 Related work
15.3 System model of FFT-NOMA
15.4 Uplink and downlink NOMA network
15.5 MIMO-NOMA systems
15.5.1 Resource allocation
15.5.2 User clustering
15.5.3 Monotonic optimization
15.5.4 Combinatorial relaxation
15.5.5 Power allocation in NOMA
15.5.6 Security and privacy in 5G systems
15.6 Results and discussions
15.7 Conclusions and future scope
References
16 A generative adversarial network-based approach for mitigating inference attacks in emerging wireless networks
Abstract
16.1 Introduction
16.2 Related work
16.3 Problem statement and proposed solution
16.3.1 What is an inference attack?
16.3.2 MaskGAN: our proposed solution
16.3.3 Research questions
16.4 Threat model
16.4.1 Solution overview
16.4.2 Audio features representation
16.4.3 Neural network models
16.4.4 Noise generation methodology
16.4.5 MaskGAN overview
16.4.6 Dataset, developmental tools, hardware, and software
16.5 Experimental approach
16.5.1 Generate noise signals with GAN
16.5.2 Measuring the degree of randomness in noise signals
16.5.3 Perform inference attacks on original audio samples
16.5.4 Mitigate sound inference attacks
16.5.5 Evaluation
16.6 Results
16.6.1 Baseline inference accuracy
16.6.2 Mitigated inference accuracy
16.6.3 Semantic preservation factor
16.6.4 Randomness to mitigation relationship
16.7 Discussion
16.7.1 White noise and randomness
16.7.2 Mitigating privacy inference leakage in digital space vs. physical space
16.8 Conclusion
Acknowledgment
References
17 Adversarial resilience of self-normalizing convolutional neural networks for deep learning-based intrusion detection systems
Abstract
17.1 Introduction
17.2 Related work
17.3 Background – adversarial machine learning
17.3.1 Adversarial taxonomy
17.3.2 Generating adversarial samples
17.4 Problem definition and proposed study
17.4.1 Problem definition
17.4.2 Proposed study
17.4.3 Threat model
17.5 Experimental approach
17.6 Solution description
17.6.1 SCNN
17.6.2 Activation functions
17.6.3 Weight initialization
17.6.4 Dropout
17.7 Experimental setup
17.7.1 Hardware platform
17.7.2 Development platform and tools
17.7.3 Dataset description
17.7.4 Dataset preparation
17.7.5 Generating the adversarial samples
17.7.6 Evaluation metrics
17.8 Results
17.8.1 Classification accuracy of CNN vs. SCNN for IDSs
17.8.2 AR of CNN vs. SCNN for IDSs
17.8.3 Classification accuracy of CNN vs. SCNN for image classification
17.8.4 AR of CNN vs. SCNN for image classification
17.9 Discussion
17.9.1 Comments on CNNs vulnerability to adversarial samples
17.9.2 Why does self-normalization make SCNN perform better than CNN in the context of adversarial resilience?
17.10 Conclusion
Acknowledgment
References
18 Legal frameworks for security schemes in wireless communication systems
Abstract
18.1 Introduction
18.1.1 Contributions
18.1.2 Chapter organization
18.2 The evolution of wireless networks
18.3 Privacy and security schemes in wireless communication systems
18.4 6G wireless network security schemes
18.5 Security framework requirements
18.5.1 Customers and subscribers
18.5.2 Network service providers
18.5.3 Public authorities
18.6 Legal frameworks for wireless network security
18.7 Security legal principles
18.7.1 Compliance
18.7.2 Data protection
18.7.3 Quality of Service
18.7.4 Conflict resolutions
18.8 Ethics and moral principles
18.9 Limitations of the study
18.10 Conclusion and recommendations
References
19 Design of a quantum true random number generator using quantum gates and benchmarking its performance on an IBM quantum-computer
Abstract
19.1 Background
19.1.1 Random numbers
19.1.2 Importance of randomness
19.1.3 Applications of random numbers
19.1.4 Quantum randomness in cryptography
19.1.5 Quantum information processing
19.1.6 Highlights of the proposed work
19.2 Literature survey
19.2.1 Methods of generating random numbers
19.2.2 Survey of pseudorandom number generators
19.2.3 Physical random number generator
19.2.4 Survey of true random number generators
19.2.5 Unpredictable random number generators
19.2.6 Quantum random number generator
19.3 Preliminaries
19.3.1 Dirac notation
19.3.2 Quantum system
19.3.3 Qubit
19.3.4 Bloch sphere
19.3.5 Evolution of a quantum system
19.4 Proposed method
19.4.1 Qiskit quantum programming
19.4.2 Scheme of random number generator
19.5 Testing random number generators statistically
19.5.1 Restart experiment
19.5.2 Statistical test suite – autocorrelation analysis
19.5.3 National Institute of Standards and Technology (NIST) SP 800-22
19.5.4 NIST 800-90B statistical test
19.6 Conclusion and future scope
References
20 Security challenges and prospects of 6G network in cloud environments
Abstract
20.1 Introduction
20.1.1 The primary contribution of this chapter is as follows
20.1.2 Chapter organization
20.2 6G network issues and solutions
20.2.1 Secure and privacy issue in 6G network transmission technology
20.3 Application of AI in 6G network
20.4 Application of blockchain security in 6G network
20.4.1 Intelligent resource management
20.4.2 Elevated security features
20.5 Security challenges of 6G networks and cloud environment
20.5.1 The 6G technologies: security and privacy issues
20.6 Security challenges in cloud environment
20.6.1 Important concepts in cloud security
20.6.2 Virtualization elements
20.6.3 Trust
20.7 Security requirements for 6G network in cloud environment
20.8 AI solution to 6G privacy and security issues in cloud environment
20.9 Conclusions
Acknowledgment
References
Index
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