AI-Driven Security for Next-Generation IoT Systems
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Beschreibung
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.
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.
Covers cutting-edge AI methods such as Deep Q-Networks and GNNs for IoT security Explores hybrid models combining deep learning, XGBoost, and blockchain for robust IDS Includes practical schemes like PUF-based authentication for real-world use cases
Autor*in
Mourade Azrour
Themen in »AI-Driven Security for Next-Generation IoT Systems«
Authentication Protocols Internet of Things Cybersecurity Authentication intrusion detection systems Malicious Detection Artificial Neural Networks Advanced Network Surveillance Deep Learning Blockchain security Next-Generation IoT IDS Vulnerability RFID