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Full Description
This book focuses on the integration of Artificial Intelligence (AI) technology for securing IoT next systems, exploring a comprehensive collection of recent techniques and strategies aimed at protecting these complex networks. Moreover, it highlights the alteration from standard security techniques to sophisticated, vulnerability-managed frameworks that can adapt flexibly to emerging threats. It also discusses the important frameworks proposed for the efficient implementation of IoT systems to provide enhanced security and privacy.
This book highlights the requirement of harnessing advanced artificial intelligence and machine learning approaches to address the evolving landscape of IoT threats, underscoring the intersection of security, scalability, and automation in next-generation IoT environments. Subsequently, the chapters investigate the fundamental methodologies and innovations transforming IoT security. Hence, topics treated range from the assessment of deep learning approaches for intrusion detection to the development of multi-factor authentication schemes based on elliptic curve cryptography.
This book is appropriate for advanced-level students in computer science and junior researchers, who are studying relevant subjects such as the Internet of Things, cybersecurity, wireless communications, and artificial intelligence. Researchers, cybersecurity specialist and professionals working in advanced IoT, data security, artificial intelligent applications or similar fields will want to purchase this book as well.
Contents
1 Authentication in the Internet of Medical Things: review and comparative study.- 2 ECC based Anonymous and Multi-factor Authentication Scheme for IoT Environment.- 3 Implementing Zero-Knowledge Authentication in the Internet of Medical Things.- 4 Machine and Deep Learning Techniques in IoT Security: A Comprehensive Survey.- 5 Comparative analysis of intrusion detection systems in Industry 5.0 systems.- 6 A Novel Intrusion Detection System for IoT in Smart Agriculture Using Crowd Wisdom and RBF Neural Networks.- 7 IoT-Based Smart Farming Security: A Malicious Detection Approach Using Artificial Neural Networks.- 8 Exploring IoT Security Risks and Protective Strategies in Industry 5.0.- 9 Advanced Network Surveillance and Security Enhancement Through Deep Learning.- 10 Blockchain and IoT Applications: Opportunities, and Challenges.- 11 Blockchain-driven security solutions for IoT.
