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Securing the Internet of Things

Edited by Abhishek Kumar, Rashmi Soni, and Arfan Ghani
Copyright: 2026 | Expected Pub Date: 2026
ISBN: 9781394466665 | Hardcover |
474 pages
Price: $225 USD

One Line Description
Seamlessly bridging rigorous academic theory with real-world implementation, this definitive guide equips you with the advanced architectural strategies and quantum-resistant frameworks needed to secure the next generation of hyper-connected systems.

Description
The Internet of Things (IoT) represents a paradigm shift in global connectivity, interconnecting billions of smart devices, industrial systems, and autonomous platforms. While this hyper-connectivity enhances operational efficiency and automation, it exposes critical vulnerabilities across personal, corporate, and national infrastructures. This volume provides a comprehensive, end-to-end analysis of IoT security, bridging theoretical foundations with real-world technical execution. Across its pages, leading paradigms in hardware-level safeguards, firmware integrity, secure communication protocols, and data privacy are systematically examined. The text further investigates cutting-edge defenses, including artificial intelligence and machine learning for threat mitigation, alongside the transformative potential of blockchain and quantum-resistant cryptography. Combining scholarly analysis with actionable architectural insights, this book is an indispensable reference for researchers, academics, and industry practitioners navigating the next generation of secure IoT ecosystems.

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Author / Editor Details
Abhishek Kumar, PhD is an Associate Professor in the School of Electronics and Electrical Engineering at Lovely Professional University, India. He has authored one book, edited three books, and published more than 50 research papers in refereed journals and conferences, and more than 30 Indian patents and copyrights. His research interests include FPGA system design, hardware security, low-power CMOS circuits, and the combination of VLSI designs and machine learning.

Rashmi Soni, PhD is a Postdoctoral Researcher in the Computer Science and Engineering Department at Amity University, Dubai, and an Associate Professor in the Information Science and Engineering Department at the Dayananda Sagar Academy of Technology and Management, Bengaluru. She has published ten patents and a number of chapters and books, and serves as an editor for several book series. Her research interests include cloud computing, IoT, bioinformatics, AI, AR and VR, and image processing.

Arfan Ghani, PhD is a Professor of Computer Engineering at the American University of Ras Al Khaimah in the United Arab Emirates, with more than 18 years of experience. He has published more than 80 research papers and collaborates with research groups in the UK, Pakistan, Germany, India, and the United Arab Emirates. He is an academic and technologist specialising in AI hardware accelerators, IoT, neuromorphic engineering, computer vision, and precision neurotechnology.

Table of Contents
Preface
Introduction
1. Ensemble Machine Learning for High-Accuracy Prediction of IoT Privacy Compliance (TC) Metrics: A Comparative Regression Analysis
Mirza Samiulla Beg
1.1 Problem Statement
1.2 Objective
1.3 Introduction
1.3.1 The Landscape of IoT and Privacy Imperatives
1.3.2 The Role of Predictive Modeling in Compliance
1.3.3 Rationale for Algorithm Selection
1.4 Literature Review
1.5 Proposed Methodology with Algorithm
1.5.1 Data Preparation and Partitioning
1.5.2 Model Development and Comparison
1.5.3 Champion Algorithm: Ensemble Modeling
1.5.4 Model Evaluation
1.5.5 Variable Importance Analysis
1.6 Flow Chart Steps
1.7 Result
1.7.1 Model Comparison Performance
1.7.2 Key Fit Statistics for Champion Model
1.7.3 Most Important Variables
1.7.4 Visual Assessment: Actual vs. Predicted
1.8 Discussion
1.9 Conclusion
1.10 Summary
References
2. AI and Machine Learning-Enabled Adaptive Security Framework for IoT Networks
Sridharan C., Charumathi M., Haritha M., Aruneshwar E.M. and Jayapratha T.
2.1 Introduction
2.2 Literature Review
2.3 Proposed System
2.3.1 Intelligent Data Acquisition and Preprocessing
2.3.2 Collaborative Learning and Federated Intelligence
2.3.3 Decision Module and Adversarial Detection
2.3.4 Result and Discussion
2.4 Experimental Work
2.4.1 Implementation Overview
2.4.2 Performance Evaluation
2.4.3 Explainability and Interpretability Analysis
2.4.4 Comparative Discussion
2.4.5 Summary of Findings
2.4.6 Discussion
2.5 Conclusion
References
3. Analyzing the Performance of Big Data Clustering Based
on Augmented Cat Swarm Optimization Method Using Evolutionary Computing
R. Venkat, Amjan Shaik, Martin Margala and Prasun Chakrabarti
3.1 Introduction
3.2 Related Works
3.3 Background Model
3.3.1 Improvised Fuzzy C-Means
3.3.2 Function Parameter
3.3.3 Computational Model
3.3.4 Cat Swarm Optimization
3.3.4.1 Basic Algorithms Steps
3.4 Proposed Methodology
3.4.1 Augmented Cat Swarm Optimization
3.4.1.1 Algorithm Steps for ACSO Algorithm
3.4.1.2 Differential Evolution
3.4.2 Multi-Layered Convolutional Neural Network (MLCNN)
3.5 Evaluating Performance
3.5.1 Performance Evaluation Metrics
3.5.2 Dataset Description
3.6 Conclusion
Bibliography
4. Architectural Paradigms of Edge Artificial Intelligence:
A Comprehensive Framework for Intelligent Computing at the Edge
Praveen Kumar Malik
4.1 Introduction
4.2 What is Edge Artificial Intelligence?
4.2.1 Definition
4.2.2 Why Edge AI is Important
4.2.3 Quicker Decision-Making
4.2.4 The Usage of Internet Got Reduced
4.2.5 Privacy and Security Improvement
4.2.6 Offline Functionality
4.2.7 Related Concepts
4.2.8 Cloud AI
4.2.9 Fog Computing
4.2.10 Mist Computing
4.3 Design Goals and Challenges
4.4 Different Types of Edge AI Architectures
4.4.1 Device-Centric Architecture
4.4.2 Advantages of Device-Centric Architecture
4.4.2.1 Quicker Response (No Internet Delay)
4.4.2.2 Secure Data Privacy
4.4.2.3 Offline Functionality
4.4.3 Disadvantages of Device-Centric Architecture
4.4.3.1 Limited Memory and Processing Power
4.4.3.2 Difficult to Update or Retrain Models
4.4.4 Real-World Example
4.4.5 Edge Gateway or Edge Server Architecture
4.4.5.1 Advantages of Edge Server-Centric Architecture
4.4.5.2 Disadvantages of Edge Server-Centric Architecture
4.4.6 Hierarchical or Multi-Layer Architecture
4.4.6.1 Benefits of Hierarchical Architecture
4.4.6.2 Drawbacks of Hierarchical Architecture
4.4.6.3 Advantages of Federated Learning
4.4.6.4 Challenges of Federated Learning
4.4.7 Model Deployment and Optimization in Edge AI
4.4.7.1 Techniques Used in Edge Artificial Intelligence
4.4.7.2 Updating Models
4.4.8 Hardware for Edge AI
4.4.8.1 Common Hardware Types
4.4.8.2 Framework for Edge AI Architecture
4.4.9 Case Studies of Edge AI Applications
4.4.9.1 Self-Driving Cars
4.4.9.2 Smart Surveillance
4.4.9.3 Challenges of Edge AI
4.4.10 Conclusion: The Future of Edge Artificial Intelligence
Bibliography
5. Artificial Intelligence and Machine Learning in IoT Security
Krishan Arora
5.1 Introduction
5.2 IoT Security Challenges
5.2.1 Device-Level Challenges
5.2.2 Network-Level Challenges
5.2.3 Application-Level Problems
5.3 AI/ML and IoT Security
5.4 IoT Security Using Machine Learning
5.4.1 Supervised Learning
5.4.2 Unsupervised Learning
5.4.3 Semi-Supervised Learning
5.4.4 Deep Learning
5.4.5 Reinforcement Learning (RL)
5.5 Artificial Intelligence Intrusion Detection Systems (IDS)
5.6 Case Studies and Applications
5.6.1 Smart Homes
5.6.2 Healthcare IoT
5.6.3 Industrial IoT
5.6.4 Smart Cities
5.7 AI/ML IoT Security Difficulties
5.8 Future Directions
5.9 Conclusion
References
6. Blockchain-Enabled Edge Computing for IoT Security and Data Processing
Ajaybeer Kaur and Indu Bala
6.1 Introduction
6.1.1 Overview of Blockchain Technology
6.1.2 Real-Time Data Processing
6.1.3 Growing Importance of Edge Processing
6.2 Literature Review
6.3 IoT Recommended Method and Security Data Analysis
6.3.1 Blockchain and Edge Computing Integrated into an IoT Framework
6.3.2 Simple Data Fusion Technique
6.3.3 Safe and Confidential Algorithms for Anomaly Detection and Localization
6.4 Experimental Results
6.4.1 Data Fusion Algorithm Test
6.4.2 Data Packet Overhead
6.4.3 Comparative Study
6.5 Conclusion and Future Scope
References
7. Internet of Things: Architectures, Protocols, and Enabling Technologies
Geetika, Aarti and Pardeep Kumar
7.1 Introduction
7.2 IoT Architectural Models
7.2.1 The Five-Layer Architecture
7.3 Technologies and Protocols for Communication
7.3.1 Protocols at the Application Layer
7.3.2 Network Layer Technologies and Protocols
7.4 The Cloud-Edge Continuum
7.5 IoT Security: A Major Obstacle
7.5.1 Important Security Issues
7.5.2 Technologies and Solutions for Security
7.6 Next-Generation IoT Enabling Technologies
7.6.1 Decentralized Trust with Blockchain
7.6.2 Networking That is Defined by Software (SDN)
7.6.3 Digital Twin Technology
7.7 Key Issues and Prospective Research Paths
7.8 Conclusion
References
8. Securing the Internet of Things: IoT in Network Security
Kamlesh Modi, Sachin Nagar and Jayshree Dasa
8.1 Introduction to IoT Network Security
8.1.1 Why Traditional Network Security Fails in IoT
8.1.2 The IoT Three-Layer Architecture (Perception, Network, Application) and Its Vulnerabilities
8.1.3 Network Layer (The Transport Layer)
8.1.4 Application Layer (The Service Layer)
8.1.5 Chapter Objectives and Structure
8.2 The IoT Network Threat Landscape: A Taxonomy of Attacks
8.2.1 Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
8.2.2 Volume-Based (Flooding) Attacks
8.2.3 Protocol Exploit Attacks
8.2.4 Amplification and Reflection Attacks
8.2.5 Application-Layer Attacks
8.2.6 Routing and Forwarding Attacks (Sinkhole, Selective
Forwarding, Wormhole)
8.2.7 Man-in-the-Middle (MITM) and Eavesdropping Attacks
8.2.8 Eavesdropping (The “Sniffing” Attack)
8.2.9 Man-in-the-Middle (MITM) Attack
8.2.10 Identity and Spoofing Attacks
8.2.11 Major IoT Network Breaches
8.3 Fundamental Defense Mechanisms for IoT Networks
8.3.1 Lightweight Cryptography
8.3.2 Authentication and Access Control Protocols for IoT
8.3.3 Secure Communication Protocols (DTLS, CoAP Security)
8.3.4 Network Segmentation and Isolation Strategies: The Digital Quarantine
8.4 Advanced Security Paradigms: Intelligent and Adaptive Defense
8.4.1 IoT Network Intrusion Detection Systems (NIDS)
8.4.2 NIDS Placement Strategies
8.4.3 Network Intrusion Prevention Systems (NIPS) for IoT
8.4.3.1 From Passive Monitoring to Active Mitigation
8.4.3.2 Advanced Mitigation Techniques
8.4.3.3 Challenge of the False Positives in the Critical IoT
8.4.4 Applying Machine Learning (ML) and Deep Learning (DL) for Threat Detection
8.4.4.1 Using CNN, RNN/LSTM for Anomaly Detection
8.4.4.2 Supervised vs. Unsupervised Learning Approaches
8.4.5 Software-Defined Networking and Its Role in Centralized Security Management for IoT
8.4.5.1 The Architecture: Brain vs. Muscle
8.4.5.2 The “Closed-Loop” Intelligent Defense System
8.4.5.3 Security Resilience and Fault Tolerance in Virtualized IoT Networks (Cloud-RAN)
8.4.6 Case Studies of IoT-Specific NIDS
8.5 Future Directions and Open Research Challenges
8.5.1 The Rise of AI-Driven and Self-Healing IoT Networks
8.5.2 Integrating Blockchain for Decentralized Trust and Security
8.5.3 Scalability, Interoperability, and Standardization Challenges (The Growing Pains)
8.6 Conclusion and Final Thoughts
References
9. Energy-Aware IoT Secure Communication Protocol
Kavyashree M. K. and Megha K. M.
9.1 Introduction
9.2 Experimental Methods and Materials
9.2.1 Network Model
9.2.2 Energy-Aware Trust Metric (ETM) Computation
9.2.3 Adaptive Trust-Verification Interval Control
9.2.4 Radio Energy Consumption Model
9.2.5 Malicious Node Classification and Secure Forwarding Strategy
9.2.6 Lightweight Authentication Using PUF-Derived Keys
9.3 Results and Discussion
9.3.1 Security Evaluation of the Suggested Protocol
9.3.2 Results
9.4 Conclusion
References
10. A Comprehensive Review of Security Architectures and Protocols for Ensuring Trust in Internet of Things Ecosystems
Praveen Kumar Malik
10.1 Introduction
10.2 Overview of the Internet of Things (IoT)
10.2.1 Definition and Characteristics
10.2.1.1 Interconnectivity
10.2.1.2 Heterogeneity
10.2.1.3 Autonomy
10.2.1.4 Context-Awareness
10.3 IoT Ecosystem and Architecture
10.4 Applications of IoT
10.4.1 Healthcare
10.4.2 Smart Cities
10.4.3 Industrial IoT (IIoT)
10.4.3.1 Agriculture
10.4.3.2 Home Automation
10.5 Security Challenges in IoT Systems
10.5.1 Threat Landscape
10.5.1.1 Eavesdropping and Data Interception
10.5.1.2 Device Hijacking (“Botnets”)
10.5.1.3 Denial of Service (DoS) and Distributed DoS (DDoS) Attacks
10.5.1.4 Malware Injection
10.5.1.5 Firmware Manipulation
10.5.2 Privacy and Trust Issues
10.5.3 Constraints in IoT Devices
10.6 IoT Security Architectures
10.6.1 Layered Security Frameworks
10.6.2 Cloud-Edge-Fog Architectures
10.6.3 Blockchain-Based Architectures
10.7 Security Protocols for IoT
10.7.1 Communication Security Protocols
10.7.2 Authentication and Key Management Protocols
10.7.3 Lightweight Cryptographic Protocols
10.8 Trust Management Models in IoT
10.8.1 Reputation-Based Systems
10.8.2 Blockchain and Smart Contracts
10.8.3 AI and Machine Learning for Trust Evaluation
10.9 Comparative Analysis of Security Architectures and Protocols
10.10 Future Trends and Research Directions
10.11 Conclusion
References
11. Artificial Intelligence and Machine Learning for Adaptive IoT Security
Prem Vijay Rao, Aryan Barot and Monica Gahlawat
11.1 Introduction
11.1.1 IoT Architecture
11.1.1.1 Perception Layer (PL)
11.1.1.2 Network Layer (NL)
11.1.1.3 Application Layer (AL)
11.1.2 IoT Security
11.1.2.1 Security Challenges in the Perception Layer (PL)
11.1.2.2 Security Challenges in the Network Layer (NL)
11.1.2.3 Security Challenges in Application Layer (AL)
11.1.3 Role of Artificial Intelligence and Machine Learning in IoT
11.1.3.1 Anomaly Detection
11.1.3.2 Real-Time Threat Monitoring
11.1.3.3 Automated Security Response
11.1.3.4 Intrusion Detection and Prevention
11.1.3.5 Adaptive Learning and Continuous Improvement
11.1.4 Motivation and Scope
11.2 IoT Security Landscape
11.2.1 Threats and Vulnerabilities in IoT Networks
11.2.1.1 Weak Device Authentication
11.2.1.2 Outdated or Unpatched Firmware
11.2.1.3 Insecure Communication Channels
11.2.1.4 Limited Device Resources
11.2.1.5 Physical Exposure and Tampering
11.2.1.6 Lack of Network Segmentation
11.2.1.7 Data Confidentiality and Privacy Risks
11.2.2 Why These Vulnerabilities Matter in AI/MLBased IoT Security
11.2.3 Common Attacks on IoT Devices
11.2.3.1 Malware and Botnets
11.2.3.2 Denial-of-Service (DoS) Attacks
11.2.3.3 Data Breaches and Privacy Threats
11.2.4 Security Requirements for IoT
11.2.4.1 Strong Authentication
11.2.4.2 Secure Communication
11.2.4.3 Regular Updates and Patch Management
11.2.4.4 Access Control and Least Privilege
11.2.4.5 Data Protection and Privacy
11.2.4.6 Device Monitoring and Anomaly Detection
11.2.4.7 Physical Security
11.2.4.8 Scalability and Interoperability
11.2.4.9 Secure Boot and Trusted Execution
11.2.4.10 Resilience and Recovery
11.3 AI and ML Techniques for IoT Security
11.3.1 Machine Learning Algorithms for Threat Detection
11.3.1.1 Supervised Learning Approaches
11.3.1.2 Unsupervised Learning Approaches
11.3.1.3 Reinforcement Learning in Security
11.3.2 Deep Learning Models for IoT Security
11.3.2.1 Neural Networks and Convolutional Neural Networks (CNNs)
11.3.2.2 Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs)
11.3.3 AI-Driven Anomaly Detection and Intrusion Prevention
11.4 AI/ML Applications for IoT Security
11.4.1 Smart Homes and Buildings
11.4.1.1 Important Application
11.4.2 Industrial IoT - Security
11.4.2.1 Key Applications
11.4.3 Healthcare IoT Security
11.4.3.1 Key Applications
11.4.4 Smart Cities and Transportation Systems
11.4.4.1 Key Applications
11.5 Challenges and Limitations
11.5.1 Scarce and Poor-Quality Data
11.5.2 Computational Constraints of IoT Devices
11.5.3 Adversarial Attacks on AI/ML Models
11.5.4 Privacy and Ethical Considerations
11.6 Emerging Trends and Future Directions
11.6.1 Federated Learning for Distributed IoT Security
11.6.2 Explainable AI (XAI) for Transparent Security Decisions
11.6.3 Edge AI and Real-Time Threat Mitigation
11.6.4 Integration with Blockchain and Secure IoT Frameworks
11.7 Case Studies
11.7.1 Industrial Internet of Things (IIoT): Real-World Implementation
11.7.2 AI-Based Intrusion Detection Systems (IDS) in IoT Networks
11.7.3 Comparative Analysis of Machine Learning Approaches for IoT Security
11.7.4 Lessons Learned and Best Practices
11.7.4.1 Quality of the Data Determines Accuracy
11.7.4.2 Edge-Based Processing Reduces Latency
11.7.4.3 AI Needs to Be Regularly Updated
11.7.4.4 Role of Human Oversight Remains Relevant
11.7.4.5 Multi-Layered Security is More Effective
11.7.4.6 Trust is Established through Transparency
11.8 Conclusion
11.8.1 Summary of Key Findings
11.8.2 The Road Ahead for AI-Driven IoT Security
11.8.2.1 Edge AI Use Cases to Increase
11.8.2.2 Rise in the Adoption of Federated Learning
11.8.2.3 More Transparent and Explainable AI
11.8.2.4 Integration of Blockchain for Trust Management
11.8.2.5 Autonomous Security Systems
References
12. Leveraging Explainable AI for the Health Sector
Rocky Kumar, Hemant K. Upadhyay, Himanshi Sharma and Vatsala Sharma
12.1 Introduction
12.1.1 Background
12.1.2 Importance of Explainability in Healthcare
12.1.3 Scope of the Chapter
12.1.4 Objective of the Chapter
12.2 Fundamentals of Explainable AI (XAI)
12.2.1 XAI and Explainability (Global vs. Local)
12.2.1.1 Types of XAI Explanations: Global vs. Local
12.2.2 Common XAI Techniques (SHAP, LIME)
12.2.2.1 SHAP (Shapley Additive Explanations)
12.2.2.2 Advantages and Limitations of SHAP
12.2.2.3 Key Healthcare Scenarios for SHAP
12.2.2.4 LIME (Local Interpretable Model-Agnostic Explanations)
12.2.2.5 The Process of LIME can be Broken Down into the Following Key Steps
12.2.2.6 Limitations of LIME
12.2.2.7 Application of LIME in Healthcare
12.2.2.8 Addressing Lime’s Instability: Opti LIME
12.3 AI in Healthcare: Opportunities and Challenges
12.3.1 Opportunities of AI in Healthcare
12.3.2 Challenges of AI in Healthcare
12.3.3 Applications of AI in Healthcare
12.3.4 Key Challenges of AI in Healthcare
12.4 Methodologies for Explainability in Healthcare AI
12.4.1 Various Methodologies for XAI
12.4.1.1 Intrinsic Interpretability Methods
12.4.1.2 Feature Importance-Based Explainability
12.4.2 Rule-Based and Decision Tree Models
12.4.3 Attention Mechanisms in Deep Learning Models
12.4.4 Case-Based Reasoning and Example-Based Explanations
12.5 Real-World Applications of XAI in Healthcare
12.5.1 Medical Diagnostics and Imaging
12.5.1.1 Predictive Analytics for Patient Outcomes
12.5.1.2 Personalized Treatment Plans
12.5.1.3 Clinical Decision Support Systems (CDSS)
12.5.1.4 Robotic Surgery
12.5.2 Explainable AI for Medical Imaging (Radiology, Pathology)
12.5.3 XAI in Disease Diagnosis and Prediction (Diabetes, Cancer, Cardiology)
12.5.4 Explainability in Electronic Health Records (EHR) Systems
12.5.5 AI in Personalized Treatment and Drug Discovery
12.5.5.1 Patient-Doctor Connectivity
12.5.5.2 Continuous Learning to Make Clinical Decisions
12.5.5.3 Diagnosis
12.5.5.4 Monitoring Treatment Progress
12.5.5.5 Precision Medicine and Targeted Therapies
12.6 Drug Discovery
12.7 Future Directions
12.7.1 Integrating XAI into Clinical Workflows
12.7.1.1 Importance of XAI in Clinical Decision Support Systems (CDSS)
12.7.1.2 Challenges in Integrating Explainable Artificial Intelligence into Clinical Workflows
12.7.1.3 Interoperability and Heterogeneous System Architectures
12.7.1.4 Workflow Integration and Adaptive Implementation
12.7.1.5 Regulatory Compliance and Ethical Governance
12.7.1.6 Clinician Trust and Cognitive Alignment
12.7.2 Strategies for Effective XAI Integration
12.7.2.1 Enhancing Clinical Decision Support (CDS) Alerts
12.7.2.2 Utilizing Local and Global Explanations
12.7.2.3 Implementation of XAI-Based CDSS Frameworks
12.7.2.4 Integration with Electronic Health Records (EHRs)
12.7.3 Future Directions and Recommendations
12.7.3.1 Development of Standardized Guidelines
12.7.3.2 Advancing Model Transparency and Usability
12.7.3.3 Enhancing Model Generalizability
12.7.4 Future Research Directions and Emerging Trends
12.7.4.1 Explainability-by-Design
12.7.4.2 Situated (Context-Dependent) Explanations
12.7.4.3 Multi-Faceted and Explanations through Causality
12.7.4.4 Conscientious and Moral Artificial Intelligence
12.7.4.5 Human-AI Collaboration in Industry 5.0 1.5
12.7.4.6 Regulation and Compliance in XAI
12.7.5 Emerging Trends
12.7.5.1 Integration of XAI with Augmented and Virtual Reality
12.7.5.2 AI-Generated Natural Language Explanations
12.7.5.3 Self-Explaining AI Models
12.7.5.4 Explainable AI for Decision Support Systems
12.7.5.5 Man-Machine Parallel Living in Industry 5.0
12.7.5.6 AI-Driven Explainability for Automated Compliance
12.8 Implications for Healthcare Practitioners and Policymakers
12.8.1 Digital Health Technology Integration
12.8.2 Implications for Policymakers
12.9 Conclusions
References
13. Smart Bin Overflow Management Using GPS-GSM
S. Towseef Ahmed and K.C.T. Swamy
13.1 Introduction
13.2 Investigational Approach
13.2.1 Working Principle
13.3 Advantages
13.4 Applications
13.5 Results and Discussion
13.6 Conclusion
References
14. Secure Communication Protocols for IoT
Tharan A., Rahul R., Thamilarasan G. P., Majid Hussain J. and Vasanthakumar C.
14.1 Introduction
14.1.1 Background
14.1.2 The Safety of Secure Communication
14.1.3 IoT Communication Security Problems
14.1.4 The Role of Communication Protocols in IoT Security
14.2 IoT Communication Architecture and Threat Model
14.2.1 Introduction
14.2.2 IoT Communication Architecture
14.2.2.1 Perception Layer (Device Layer)
14.2.2.2 Network Layer (Transmission Layer)
14.2.2.3 Application Layer
14.2.2.4 Edge/Fog and Cloud Layers
14.2.3 IoT Threat Model
14.2.3.1 Adversary Capabilities
14.2.3.2 Common IoT Attacks
14.2.3.3 Threat Mapping with Security Objectives
14.2.4 Security Requirements Derived from the Threat Model
14.3 IoT Communication Models and Protocol Stack
14.3.1 Introduction
14.3.2 IoT Communication Models
14.3.2.1 Device-to-Device (D2D) Communication
14.3.2.2 Device-to-Gateway Communication
14.3.2.3 Device-to-Cloud Communication
14.3.2.4 Device-to-Edge-to-Cloud (Hybrid)
14.3.3 IoT Protocol Stack Overview
14.3.4 Key IoT Communication Protocols
14.3.4.1 The Message Queuing Telemetry Transport (MQTT)
14.3.4.2 The Constrained Application Protocol (CoAP)
14.3.4.3 Advanced Message Queuing Protocol (AMQP)
14.3.4.4 XMPP (Extensible Messaging and Presence Protocol)
14.3.4.5 LoRaWAN (Long Range Wide Area Network)
14.3.5 Security in IoT Protocol Stack
14.3.6 Interoperability and Standardisation
14.4 Secure Communication Protocols in IoT
14.4.1 Introduction
14.4.2 Core Principles of Secure IoT Communication
14.4.3 Categories of Secure Communication Protocols
14.4.4 Transport-Layer Security Protocols
14.4.4.1 Transport Layer Security (TLS)
14.4.4.2 Datagram Transport Layer Security (DTLS)
14.4.5 Network-Layer Security Protocols
14.4.5.1 IPsec (Internet Protocol Security)
14.4.5.2 RPL Security (Routing Protocol for Low-Power and Lossy Networks)
14.4.6 Application-Layer Security Protocols
14.4.6.1 OSCORE (Object Security for Constrained RESTful Environments)
14.4.6.2 ACE-OAuth (Authentication and Authorisation for Constrained Environments)
14.4.6.3 Lightweight M2M (LwM2M) Security
14.4.7 Link-Layer Security
14.4.7.1 IEEE 802.15.4 Security
14.4.7.2 LoRaWAN Security
14.4.8 Comparative Analysis of IoT Security Protocols
14.4.9 Emerging Trends and Future Directions
14.5 Lightweight Cryptography and Secure Key Management in IoT
14.5.1 Introduction
14.5.2 Design Requirements for Lightweight Cryptography
14.5.3 Lightweight Symmetric Cryptography
14.5.3.1 Block Cyphers
14.5.3.2 Stream Cyphers
14.5.3.3 Hash Functions and Message Authentication Codes
14.5.4 Lightweight Asymmetric Cryptography
14.5.4.1 Elliptic Curve Cryptography (ECC)
14.5.4.2 Post-Quantum Lightweight Cryptography
14.5.5 Secure Key Management in IoT
14.5.5.1 Key Generation
14.5.5.2 Key Distribution and Exchange
14.5.5.3 Key Storage
14.5.5.4 Key Renewal and Revocation
14.5.6 Lightweight Cryptographic Framework Integration
14.5.7 Performance Evaluation
14.5.8 Case Study: Secure Sensor Data Transmission
14.5.9 Emerging Trends and Research Directions
14.6 Emerging Technologies in Secure IoT Communication Protocols
14.6.1 Blockchain Fundamentals for IoT
14.6.2 Blockchain-Based IoT Communication Models
14.6.3 Challenges of Blockchain in IoT
14.6.4 Post-Quantum Cryptography: Implementation Considerations
14.6.5 AI and Machine Learning for Adaptive Security
14.6.5.1 AI for Anomaly Detection
14.6.5.2 Reinforcement Learning for Dynamic Protocols
14.6.5.3 Federated Learning for Privacy Preservation
14.6.6 Integration of Emerging Technologies
14.7 Secure Communication in Domain-Specific IoT Environments
14.7.1 Healthcare IoT (H-IoT)
14.7.1.1 Architecture
14.7.1.2 Security Implementation
14.7.1.3 Results and Discussion
14.7.1.4 Outcome
14.7.2 Industrial IoT (IIoT)
14.7.2.1 Architecture
14.7.2.2 Security Implementation
14.7.2.3 Results
14.7.2.4 Outcome
14.7.3 Smart City IoT
14.7.3.1 Architecture
14.7.3.2 Security Techniques
14.7.3.3 Results
14.7.3.4 Outcome
14.7.4 Comparative Analysis of Case Studies
14.7.5 Discussion and Key Insights
14.8 Future Research Directions and Emerging Trends in Secure IoT Communication
14.8.1 Introduction
14.8.2 Quantum-Resistant Cryptography for IoT
14.8.3 Artificial Intelligence and Machine Learning for Secure IoT Communication
14.8.4 Blockchain and Distributed Ledger Technologies
14.8.5 Secure Communication Through Edge and Fog Computing
14.8.6 Bio-Inspired and Quantum Communication Models
14.8.7 Standardisation and Global Collaboration
14.9 Summary of Key Findings
14.10 Conclusion
References
15. AI-Driven IoT Security: Intelligent Frameworks for Threat Detection and Adaptive Defense
Megha K. M.
15.1 Introduction
15.1.1 Problem Statement
15.1.2 Objectives
15.1.3 Motivation
15.1.4 Gap Analysis
15.2 Experimental Methods and Materials
15.2.1 Proposed Framework Architecture
15.2.2 Key Components
15.2.3 Lightweight Edge Considerations
15.2.4 Privacy-Preserving Mechanisms
15.2.5 Evaluation Metrics & Simulation Setup
15.3 Result Analysis and Comparison
15.3.1 Experimental Setup
15.3.2 Key Findings
15.3.3 Graphical Comparison
15.3.4 Comparison with Recent Works
15.4 Discussion of Results
15.5 Conclusion and Future Directions
References
16. Evolving Intelligence: AI and ML Approaches for Securing IoT Ecosystems
Vinayak G. M. Jagtap, Subhas V. Pingale and Sampat G. Deshmukh
16.1 Introduction
16.2 Formation of Connected Networks and Device Expansion
16.2.1 Fundamental IoT Structural Layers and Device Communication
16.2.2 Initial Assumptions and Overlooked Security Challenges During Rapid Growth
16.2.3 The Era of Major Security Breaches
16.2.4 The Shift toward Data-Centric Security Approaches
16.2.5 Advancements in Threat Detection with Deep Learning
16.2.6 Building Trust through Advanced Technologies
16.2.7 The Growing Threat of Adversarial Attacks
16.2.8 The Era of Network Convergence and Hybrid Technologies
16.2.9 Addressing Ethical and Privacy Concerns
16.2.10 Preparing for the Quantum Future
16.3 Conclusion
References
17. Zero Trust Architecture for IoT Security
Abhishek Kumar
17.1 Introduction
17.2 Trust in IoT
17.3 Zero Trust Implementation into IoT
17.3.1 Zero Trust Security Workflow in IoT
17.3.2 Example: Zero Trust in a Smart Temperature Sensor
17.4 Indicator to Zero Trust
17.5 Conclusion
References
Index

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Securing the Internet of Things