Wireless Communication for Cybersecurity

Cover
Title Page
Copyright Page
Contents
Preface
Chapter 1 BBUCAF: A Biometric-Based User Clustering Authentication Framework in Wireless Sensor Network
	1.1 Introduction to Wireless Sensor Network
	1.2 Background Study
	1.3 A Biometric-Based User Clustering Authentication Framework
		1.3.1 Biometric-Based Model
		1.3.2 Clustering
	1.4 Experimental Analysis
	1.5 Conclusion
	References
Chapter 2 DeepNet: Dynamic Detection of Malwares Using Deep Learning Techniques
	2.1 Introduction
	2.2 Literature Survey
		2.2.1 ML or Metaheuristic Methods for Malware Detection
		2.2.2 Deep Learning Algorithms for Malware Detection
	2.3 Malware Datasets
		2.3.1 Android Malware Dataset
		2.3.2 SOREL-20M Dataset
	2.4 Deep Learning Architecture
		2.4.1 Deep Neural Networks (DNN)
		2.4.2 Convolutional Neural Networks (CNN)
		2.4.3 Recurrent Neural Networks (RNN)
		2.4.4 Deep Belief Networks (DBN)
		2.4.5 Stacked Autoencoders (SAE)
	2.5 Proposed System
		2.5.1 Datasets Used
		2.5.2 System Architecture
		2.5.3 Data Preprocessing
		2.5.4 Proposed Methodology
		2.5.5 DeepNet
		2.5.6 DBN
		2.5.7 SAE
		2.5.8 Categorisation
	2.6 Result and Analysis
	2.7 Conclusion & Future Work
	References
Chapter 3 State of Art of Security and Risk in Wireless Environment Along with Healthcare Case Study
	3.1 Introduction
	3.2 Literature Survey
	3.3 Applications of Wireless Networks
	3.4 Types of Attacks
		3.4.1 Passive Attacks
		3.4.2 Release of Message Contents
		3.4.3 Traffic Analysis
		3.4.4 Eavesdropping
	3.5 Active Attacks
		3.5.1 Malware
		3.5.2 Password Theft
		3.5.3 Bandwidth Stealing
		3.5.4 Phishing Attacks
		3.5.5 DDoS
		3.5.6 Cross-Site Attack
		3.5.7 Ransomware
		3.5.8 Message Modification
		3.5.9 Message Replay
		3.5.10 Masquerade
	3.6 Layered Attacks in WSN
		3.6.1 Attacks in Physical Layer
		3.6.2 Attacks in Data Link Layer
		3.6.3 Attacks in Network Layer
		3.6.4 Attacks in Transport Layer
		3.6.5 Attacks in Application Layer
	3.7 Security Models
		3.7.1 Bio-Inspired Trust and Reputation Model
		3.7.2 Peer Trust System
	3.8 Case Study: Healthcare
		3.8.1 Security Risks in Healthcare
		3.8.2 Prevention from Security Attacks in Healthcare
	3.9 Minimize the Risks in a Wireless Environment
		3.9.1 Generate Strong Passwords
		3.9.2 Change Default Wi-Fi Username and Password
		3.9.3 Use Updated Antivirus
		3.9.4 Send Confidential Files with Passwords
		3.9.5 Detect the Intruders
		3.9.6 Encrypt Network
		3.9.7 Avoid Sharing Files Through Public Wi-Fi
		3.9.8 Provide Access to Authorized Users
		3.9.9 Used a Wireless Controller
	3.10 Conclusion
	References
Chapter 4 Machine Learning-Based Malicious Threat Detection and Security Analysis on Software-Defined Networking for Industry 4.0
	4.1 Introduction
		4.1.1 Software-Defined Network
		4.1.2 Types of Attacks
			4.1.2.1 Denial of Services
			4.1.2.2 Distributed Denial of Service
	4.2 Related Works
	4.3 Proposed Work for Threat Detection and Security Analysis
		4.3.1 Traffic Collection
			4.3.1.1 Data Flow Monitoring and Data Collection
			4.3.1.2 Purpose of Data Flow Monitoring and Data Collection
			4.3.1.3 Types of Collection
		4.3.2 Feature Selection Using Entropy
		4.3.3 Malicious Traffic Detection
			4.3.3.1 Framing of the Expected Traffic Status
			4.3.3.2 Traffic Filtering Using Regression
		4.3.4 Traffic Mitigation
	4.4 Implementation and Results
	4.5 Conclusion
	References
Chapter 5 Privacy Enhancement for Wireless Sensor Networks and the Internet of Things Based on Cryptological Techniques
	5.1 Introduction
	5.2 System Architecture
	5.3 Literature Review
	5.4 Proposed Methodology
	5.5 Results and Discussion
	5.6 Analysis of Various Security and Assaults
	5.7 Conclusion
	References
Chapter 6 Security and Confidentiality Concerns in Blockchain Technology: A Review
	6.1 Introduction
	6.2 Blockchain Technology
	6.3 Blockchain Revolution Drivers
		6.3.1 Transparent, Decentralised Consensus
		6.3.2 Model of Agreement(s)
		6.3.3 Immutability and Security
		6.3.4 Anonymity and Automation
		6.3.5 Impact on Business, Regulation, and Services
		6.3.6 Access and Identity
	6.4 Blockchain Classification
		6.4.1 Public Blockchain
		6.4.2 Private Blockchain
		6.4.3 Blockchain Consortium
	6.5 Blockchain Components and Operation
		6.5.1 Data
		6.5.2 Hash
		6.5.3 MD5
		6.5.4 SHA 256
		6.5.5 MD5 vs. SHA-256
	6.6 Blockchain Technology Applications
		6.6.1 Blockchain Technology in the Healthcare Industry
		6.6.2 Stock Market Uses of Blockchain Technology
		6.6.3 Financial Exchanges in Blockchain Technology
		6.6.4 Blockchain in Real Estate
		6.6.5 Blockchain in Government
		6.6.6 Other Opportunities in the Industry
	6.7 Difficulties
	6.8 Conclusion
	References
Chapter 7 Explainable Artificial Intelligence for Cybersecurity
	7.1 Introduction
		7.1.1 Use of AI in Cybersecurity
		7.1.2 Limitations of AI
		7.1.3 Motivation to Integrate XAI to Cybersecurity
		7.1.4 Contributions
	7.2 Cyberattacks
		7.2.1 Phishing Attack
			7.2.1.1 Spear Phishing
			7.2.1.2 Whaling
			7.2.1.3 Smishing
			7.2.1.4 Pharming
		7.2.2 Man-in-the-Middle (MITM) Attack
			7.2.2.1 ARP Spoofing
			7.2.2.2 DNS Spoofing
			7.2.2.3 HTTPS Spoofing
			7.2.2.4 Wi-Fi Eavesdropping
			7.2.2.5 Session Hijacking
		7.2.3 Malware Attack
			7.2.3.1 Ransomware
			7.2.3.2 Spyware
			7.2.3.3 Botnet
			7.2.3.4 Fileless Malware
		7.2.4 Denial-of-Service Attack
		7.2.5 Zero-Day Exploit
		7.2.6 SQL Injection
	7.3 XAI and Its Categorization
		7.3.1 Intrinsic or Post-Hoc
		7.3.2 Model-Specific or Model-Agnostic
		7.3.3 Local or Global
		7.3.4 Explanation Output
	7.4 XAI Framework
		7.4.1 SHAP (SHAPley Additive Explanations) and SHAPley Values
		7.4.1.1 Computing SHAPley Values
		7.4.2 LIME - Local Interpretable Model Agnostic Explanations
		7.4.2.1 Working of LIME
		7.4.3 ELI5
		7.4.4 Skater
		7.4.5 DALEX
	7.5 Applications of XAI in Cybersecurity
		7.5.1 Smart Healthcare
		7.5.2 Smart Banking
		7.5.3 Smart Cities
		7.5.4 Smart Agriculture
		7.5.5 Transportation
		7.5.6 Governance
		7.5.7 Industry 4.0
		7.5.8 5G and Beyond Technologies
	7.6 Challenges of XAI Applications in Cybersecurity
		7.6.1 Datasets
		7.6.2 Evaluation
		7.6.3 Cyber Threats Faced by XAI Models
		7.6.4 Privacy and Ethical Issues
	7.7 Future Research Directions
	7.8 Conclusion
	References
Chapter 8 AI-Enabled Threat Detection and Security Analysis
	8.1 Introduction
		8.1.1 Phishing
		8.1.2 Features
		8.1.3 Optimizer Types
		8.1.4 Gradient Descent
		8.1.5 Types of Phishing Attack Detection
	8.2 Literature Survey
	8.3 Proposed Work
		8.3.1 Data Collection and Pre-Processing
		8.3.2 Dataset Description
		8.3.3 Performance Metrics
	8.4 System Evaluation
	8.5 Conclusion
	References
Chapter 9 Security Risks and Its Preservation Mechanism Using Dynamic Trusted Scheme
	9.1 Introduction
		9.1.1 Need of Trust
		9.1.2 Need of Trust-Based Mechanism in IoT Devices
		9.1.3 Contribution
	9.2 Related Work
	9.3 Proposed Framework
		9.3.1 Dynamic Trust Updation Model
		9.3.2 Blockchain Network
	9.4 Performance Analysis
		9.4.1 Dataset Description and Simulation Settings
		9.4.2 Traditional Method and Evaluation Metrics
	9.5 Results Discussion
	9.6 Empirical Analysis
	9.7 Conclusion
	References
Chapter 10 6G Systems in Secure Data Transmission
	10.1 Introduction
	10.2 Evolution of 6G
	10.3 Functionality
		10.3.1 Security and Privacy Issues
			10.3.1.1 Artificial Intelligence (AI)
			10.3.1.2 Molecular Communication
			10.3.1.3 Quantum Communication
		10.3.2 Blockchain
		10.3.3 TeraHertz Technology
		10.3.4 Visible Light Communication (VLC)
	10.4 6G Security Architectural Requirements
	10.5 Future Enhancements
	10.6 Summary
	References
Chapter 11 A Trust-Based Information Forwarding Mechanism for IoT Systems
	11.1 Introduction
		11.1.1 Need of Security
		11.1.2 Role of Trust-Based Mechanism in IoT Systems
		11.1.3 Contribution
	11.2 Related Works
	11.3 Estimated Trusted Model
	11.4 Blockchain Network
	11.5 Performance Analysis
		11.5.1 Dataset Description and Simulation Settings
		11.5.2 Comparison Methods and Evaluation Metrics
	11.6 Results Discussion
	11.7 Empirical Analysis
	11.8 Conclusion
	References
About the Editors
Index
EULA