Internet of Things Security and Privacy : Practical and Management Perspectives

Internet of Things Security and Privacy Practical and Management Perspectives

Detalles Bibliográficos
Otros Autores: Awad, Ali Ismail, editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press [2024]
Edición:First edition
Materias:
Engineering.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009809018706719
Tabla de Contenidos:
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Table of Contents
  • Preface
  • Editors
  • Contributors
  • Chapter 1 Cybersecurity Risk Assessment in Advanced Metering Infrastructure
  • 1.1 Introduction
  • 1.2 Preliminaries
  • 1.2.1 Advanced Metering Infrastructure
  • 1.2.2 AMI Components
  • 1.2.3 AMI Tiers
  • 1.2.4 Information Security Risk Assessment
  • 1.3 Implementation of the AMI System's Risk Assessment
  • 1.3.1 Risk Identification Phase for the AMI System
  • 1.3.2 AMI Vulnerabilities
  • 1.3.3 Risk Profiling Phase for the AMI System
  • 1.3.4 Risk Treatment Phase for the AMI System
  • 1.4 Discussion and Recommendations
  • 1.4.1 Recommendations
  • 1.5 Conclusion
  • Acknowledgment
  • References
  • Chapter 2 A Generative Neural Network for Improving Metamorphic Malware Detection in IoT Mobile Devices
  • 2.1 Introduction
  • 2.2 Background
  • 2.2.1 Machine Learning
  • 2.2.2 Deep Learning Malware Detection
  • 2.2.3 Adversarial Machine Learning
  • 2.2.4 Generative Adversarial Networks
  • 2.2.5 Related Work
  • 2.3 Methodology
  • 2.3.1 Dataset
  • 2.3.2 Dynamic Analysis
  • 2.3.3 Data Preparation
  • 2.3.4 Image Generation
  • 2.3.5 Adversarial Samples
  • 2.3.6 Convolutional Neural Network (CNN)
  • 2.4 Experimental Design
  • 2.4.1 Experimental Setup
  • 2.4.2 Behavior Feature Extraction
  • 2.4.3 Words to Images
  • 2.4.4 Synthetic Images
  • 2.4.5 Image Classification
  • 2.5 Results and Discussion
  • 2.5.1 Assessing the Evasive Effectiveness of the Generated Samples Using a CNN Classifier
  • 2.5.2 Assessing the Effectiveness of the CNN Classifier with a Novel Dataset Including a Newly Generated Batch of Malicious Samples for Each Family Produced by the DCGAN
  • 2.5.3 Evaluation
  • 2.6 Conclusion
  • Notes
  • References
  • Chapter 3 A Physical-Layer Approach for IoT Information Security During Interference Attacks
  • 3.1 Introduction.
  • 3.2 Chapter Contributions
  • 3.3 Related Work
  • 3.4 IoT Information Security
  • 3.4.1 Background
  • 3.4.2 System Model
  • 3.5 Zero-Determinant Strategies
  • 3.6 Game-Theoretic Transmission Strategy
  • 3.6.1 Transmission Probability
  • 3.6.2 Transmission Strategy
  • 3.7 Extension to Multiple IoT Users
  • 3.7.1 Zero-Determinant Strategies
  • 3.7.2 Generalized Transmission Strategy
  • 3.8 Numerical Results
  • 3.8.1 Model Dynamics
  • 3.8.2 Simulated Use Cases
  • 3.9 Discussions
  • 3.9.1 About the Game-Theoretic Approach
  • 3.9.2 Conclusions
  • References
  • Chapter 4 Policy-Driven Security Architecture for Internet of Things (IoT) Infrastructure
  • 4.1 Introduction
  • 4.2 Related Work
  • 4.2.1 Policies and SDN
  • 4.2.2 Automatic Device Provisioning
  • 4.2.3 Secure Device Provisioning
  • 4.2.4 Machine Learning-based Classification of Devices
  • 4.2.5 IoT Security and Attacks
  • 4.3 Fundamentals of Policy-Based Network and Security Management
  • 4.3.1 Policy
  • 4.3.2 Policy-Based Network and Security Management
  • 4.3.3 Policy-Based Management Architecture
  • 4.3.4 Benefits of a Policy-Based Management Architecture
  • 4.4 IoT Network Scenario
  • 4.4.1 Types of Devices and Device Ontology
  • 4.5 Policy-Driven Security Architecture
  • 4.5.1 Device Provisioning?
  • 4.5.2 Secure Smart Device Provisioning and Monitoring Service (SDPM)
  • 4.5.3 Security Provisioning Protocol
  • 4.5.4 Digital Twin
  • 4.5.5 Policy-Based Security Application
  • 4.6 Prototype Implementation
  • 4.6.1 Network Setup
  • 4.6.2 Security Analysis
  • 4.6.3 Performance Evaluation
  • 4.7 Discussion and Open Issues
  • 4.8 Conclusion
  • References
  • Chapter 5 A Privacy-Sensitive, Situation-Aware Description Model for IoT
  • 5.1 Introduction
  • 5.2 Background
  • 5.2.1 Privacy in IoT in-Brief
  • 5.2.2 Definitions
  • 5.2.3 When MDA Meets IoT
  • 5.2.4 WoT TD In-Brief
  • 5.2.5 Case Study.
  • 5.3 Privacy-Sensitive and Situation-Aware Thing Description
  • 5.3.1 Overview
  • 5.3.2 Step 1: SituationPrivacy Metamodel Definition
  • 5.3.3 Step 2: SituationPrivacyWoTTD Metamodel Definition
  • 5.3.4 Step 3: SituationPrivacyWoTTD Model Generation
  • 5.4 Implementation
  • 5.4.1 Model Transformation
  • 5.4.2 Simulation
  • 5.4.3 Evaluation
  • 5.5 Conclusion
  • Appendix 1
  • Notes
  • References
  • Chapter 6 Protect the Gate: A Literature Review of the Security and Privacy Concerns and Mitigation Strategies Related to IoT Smart Locks
  • 6.1 Introduction
  • 6.1.1 Background
  • 6.1.2 Architecture
  • 6.1.3 Capabilities
  • 6.1.4 Access Control
  • 6.1.5 Authentication and Authorization
  • 6.2 The Privacy and Security of Smart Locks
  • 6.2.1 Smart Locks Privacy and Security From the Perspective of Researchers
  • 6.2.2 Smart Homes Privacy and Security From the Perspective of the End User
  • 6.3 Research Gaps
  • 6.4 Conclusion
  • References
  • Chapter 7 A Game-Theoretic Approach to Information Availability in IoT Networks
  • 7.1 Introduction
  • 7.2 Related Work
  • 7.3 System Model
  • 7.3.1 Spectrum-Sharing Cognitive Systems
  • 7.3.2 Problem Statement
  • 7.3.3 Primary Outage Probability
  • 7.4 Zero-Determinant Strategies
  • 7.5 Game-Theoretic Strategy for IoT Transmission
  • 7.5.1 Uncoordinated Transmission Strategy
  • 7.5.2 Special Cases
  • 7.5.3 Performance Analysis
  • 7.6 Extension to Multiple Users
  • 7.7 Numerical Results
  • 7.8 Discussions and Conclusions
  • References
  • Chapter 8 Review on Variants of Restricted Boltzmann Machines and Autoencoders for Cyber-Physical Systems
  • 8.1 Introduction to RBMs and Autoencoding
  • 8.2 Background
  • 8.2.1 Targeted Problems Using RBM's and Autoencoders
  • 8.2.2 Techniques Used for Cyber-Physical Systems Using RBMs and Autoencoders
  • 8.2.3 Detecting Network Intrusions to Ensure the Security of CPS in IoT Devices.
  • 8.3 Malware Attack Detection
  • 8.4 Fraud and Anomaly Detection
  • 8.5 Breakthroughs in CPS and their Findings
  • 8.5.1 Aim of a CPS-Based System
  • 8.5.2 Breakthroughs in CPS-Based Systems
  • 8.6 Ensuring CPS is Critical in the Modern World
  • 8.7 Evolution of CPS and its Associated Impacts
  • 8.8 Conclusion
  • Acknowledgment
  • References
  • Chapter 9 Privacy-Preserving Analytics of IoT Data Using Generative Models
  • 9.1 Introduction
  • 9.2 IoT Architecture and Applications
  • 9.3 Limitations and Challenges
  • 9.4 IoT Privacy: Definitions and Types
  • 9.5 GAN Framework
  • 9.6 Research Objectives
  • 9.6.1 Limitation of the Scope
  • 9.7 Literature Review
  • 9.7.1 Data Anonymizing
  • 9.7.2 Authentication and Authorization
  • 9.7.3 Edge Computing and Plug-In Architecture
  • 9.7.4 Using Generative Adversarial Network (GAN) in Privacy Data Analytics
  • 9.8 Overall Research Design
  • 9.9 Methodology
  • 9.9.1 Data Preparation
  • 9.10 Data Analysis and Interpretation
  • 9.10.1 Privacy Measures
  • 9.10.2 Accuracy Measures
  • 9.10.3 Incorrect Classification
  • 9.10.4 F-Measure
  • 9.10.5 Privacy
  • 9.10.6 Privacy Results Using Different Number of Epochs
  • 9.11 Conclusion and Future Work
  • References
  • Index.

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