Wireless Communication in Cyber Security

WIRELESS COMMUNICATION in CYBERSECURITY Presenting the concepts and advances of wireless communication in cybersecurity, this volume, written and edited by a global team of experts, also goes into the practical applications for the engineer, student, and other industry professionals. Rapid advanceme...

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Detalles Bibliográficos
Autor principal: Sountharrajan, S. (-)
Otros Autores: Maheswar, R., Rathee, Geetanjali, Akila, M.
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2023.
Edición:1st ed
Colección:Advances in antenna, microwave, and communication engineering.
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009811324506719
Tabla de Contenidos:
  • 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 &amp
  • 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.