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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