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Chapter 14 Enhancing IoT Security with Zero Trust Networking: Protecting Wireless Sensors, Edge Devices, and Cloud Environments

  • R. Sreelakshmi and Goutam Mali
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Cybersecurity Unlocked
This chapter is in the book Cybersecurity Unlocked

Abstract

This chapter presents a comprehensive study on the application of Zero Trust networking (ZTN) to address security vulnerabilities in Internet of Things (IoT) ecosystems. The research employs a multi-method approach, combining systematic literature review, architectural analysis, and case-based evaluation across domains such as smart cities, healthcare, and industrial IoT. The study investigates the limitations of traditional security models in dynamic, resource-constrained IoT environments and designs a ZTN-based framework tailored for such conditions. Key components of the proposed framework include lightweight authentication protocols, secure edge-based decision-making mechanisms, and encrypted data exchange workflows.

Through detailed case studies, the chapter demonstrates how ZTN principles enhance system resilience against unauthorized access and data breaches. Findings highlight the efficacy of decentralized trust enforcement and context-aware access control in improving IoT security posture. The chapter also identifies critical implementation challenges, such as scalability, integration with legacy infrastructure, and the overhead of cryptographic operations on constrained devices. The research concludes by exploring emerging trends, including the integration of artificial intelligence for dynamic policy enforcement and blockchain for distributed trust validation. Overall, the chapter provides practical insights and a scalable model for researchers and practitioners aiming to deploy Zero Trust principles in modern IoT environments.

Abstract

This chapter presents a comprehensive study on the application of Zero Trust networking (ZTN) to address security vulnerabilities in Internet of Things (IoT) ecosystems. The research employs a multi-method approach, combining systematic literature review, architectural analysis, and case-based evaluation across domains such as smart cities, healthcare, and industrial IoT. The study investigates the limitations of traditional security models in dynamic, resource-constrained IoT environments and designs a ZTN-based framework tailored for such conditions. Key components of the proposed framework include lightweight authentication protocols, secure edge-based decision-making mechanisms, and encrypted data exchange workflows.

Through detailed case studies, the chapter demonstrates how ZTN principles enhance system resilience against unauthorized access and data breaches. Findings highlight the efficacy of decentralized trust enforcement and context-aware access control in improving IoT security posture. The chapter also identifies critical implementation challenges, such as scalability, integration with legacy infrastructure, and the overhead of cryptographic operations on constrained devices. The research concludes by exploring emerging trends, including the integration of artificial intelligence for dynamic policy enforcement and blockchain for distributed trust validation. Overall, the chapter provides practical insights and a scalable model for researchers and practitioners aiming to deploy Zero Trust principles in modern IoT environments.

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Chapter 1 Emerging Cyber Threats: Challenges, Impacts, and Proactive Defenses in the Digital Age 1
  4. Chapter 2 Silent Guardians: Proactive Approaches to Modern Cyber Threats 31
  5. Chapter 3 Data Science for Threat Detection and Analysis 59
  6. Chapter 4 An Integrated Approach: Merging Cybersecurity, AI, and Threat Detection 87
  7. Chapter 5 Cybersecurity Analytics: A Review of Challenges and the Role of Machine Learning and Deep Learning in Threat Detection 103
  8. Chapter 6 Hardware-Based Authentication Techniques for Secure Data Transmission in IoT Edge Computing 141
  9. Chapter 7 Securing the IoT Networks Using a Deep Learning Paradigm for Intrusion Detection 161
  10. Chapter 8 Hybrid Malware Detection and Classification Using Explainable Deep Neural Network 177
  11. Chapter 9 Light POW for Smart Grid Communication 201
  12. Chapter 10 Zero Trust Architecture – A Beginner’s Guide 227
  13. Chapter 11 Post-quantum Cryptography for Enhanced Authentication in Mobile Data Communication: Resilience Against Quantum Attacks 265
  14. Chapter 12 Two-Factor Authentication (2FA) and Multi-factor Authentication (MFA) Solutions for Secure Mobile Data Communication 287
  15. Chapter 13 Artificial Intelligence and Machine Learning in Cybersecurity 313
  16. Chapter 14 Enhancing IoT Security with Zero Trust Networking: Protecting Wireless Sensors, Edge Devices, and Cloud Environments 343
  17. Chapter 15 Biometric Authentication Methods for Mobile Devices: Exploring Fingerprint, Face Recognition, and Iris Scanning 365
  18. Chapter 16 Robust Dynamic Voice-Based Key Generation Using Novel Fuzzy Extraction, Averaged Thresholding, and Hamming Enhancement Techniques 385
  19. Chapter 17 Enhancing Cybersecurity with Artificial Intelligence and Machine Learning Techniques 413
  20. Chapter 18 Firewall and IDS in Cybersecurity 439
  21. Index
https://doi.org/10.1515/9783111712895-014

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Chapter 1 Emerging Cyber Threats: Challenges, Impacts, and Proactive Defenses in the Digital Age 1
  4. Chapter 2 Silent Guardians: Proactive Approaches to Modern Cyber Threats 31
  5. Chapter 3 Data Science for Threat Detection and Analysis 59
  6. Chapter 4 An Integrated Approach: Merging Cybersecurity, AI, and Threat Detection 87
  7. Chapter 5 Cybersecurity Analytics: A Review of Challenges and the Role of Machine Learning and Deep Learning in Threat Detection 103
  8. Chapter 6 Hardware-Based Authentication Techniques for Secure Data Transmission in IoT Edge Computing 141
  9. Chapter 7 Securing the IoT Networks Using a Deep Learning Paradigm for Intrusion Detection 161
  10. Chapter 8 Hybrid Malware Detection and Classification Using Explainable Deep Neural Network 177
  11. Chapter 9 Light POW for Smart Grid Communication 201
  12. Chapter 10 Zero Trust Architecture – A Beginner’s Guide 227
  13. Chapter 11 Post-quantum Cryptography for Enhanced Authentication in Mobile Data Communication: Resilience Against Quantum Attacks 265
  14. Chapter 12 Two-Factor Authentication (2FA) and Multi-factor Authentication (MFA) Solutions for Secure Mobile Data Communication 287
  15. Chapter 13 Artificial Intelligence and Machine Learning in Cybersecurity 313
  16. Chapter 14 Enhancing IoT Security with Zero Trust Networking: Protecting Wireless Sensors, Edge Devices, and Cloud Environments 343
  17. Chapter 15 Biometric Authentication Methods for Mobile Devices: Exploring Fingerprint, Face Recognition, and Iris Scanning 365
  18. Chapter 16 Robust Dynamic Voice-Based Key Generation Using Novel Fuzzy Extraction, Averaged Thresholding, and Hamming Enhancement Techniques 385
  19. Chapter 17 Enhancing Cybersecurity with Artificial Intelligence and Machine Learning Techniques 413
  20. Chapter 18 Firewall and IDS in Cybersecurity 439
  21. Index
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