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...
Autor principal: | |
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Otros Autores: | , , |
Formato: | Libro electrónico |
Idioma: | Inglés |
Publicado: |
Newark :
John Wiley & Sons, Incorporated
2023.
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Edición: | 1st ed |
Colección: | Advances in antenna, microwave, and communication engineering.
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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 &
- 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.