AI-Driven Security for Next-Generation IoT Systems

Inbunden, Engelska, 2026

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2026-01-03
Mått:155 x 235 x 20 mm
Vikt:545 g
Format:Inbunden
Språk:Engelska
Antal sidor:241
Förlag:Springer Nature Switzerland AG
ISBN:9783032087836

Utforska kategorier

Elektronik och kommunikationer inom Naturvetenskap och teknik
Artificiell intelligens inom Data och IT
Hårdvara inom Data och IT

Mer om författaren

Prof. Dr. Mourade Azrour received his PhD from Faculty of sciences and Techniques, Moulay Ismail University of Meknes, Morocco. He has received his MS in computer and distributed systems from Faculty of Sciences, Ibn Zouhr University, Agadir, Morocco in 2014. Mourade currently works as computer sciences professor at the Department of Computer Science, Faculty of Sciences and Techniques, Moulay Ismail University of Meknès. His research interests include Authentication protocol, Computer Security, Internet of things, Smart systems, Machine learning and so ones. Mourade is member of the member of the scientific committee of numerous international conferences. He is also a reviewer of various scientific journals. He has published more than 163 scientific papers and book chapters. Mourade Has edited many scientific books for example: “IoT, Machine Learning and Data Analytics for Smart Healthcare”, “Blockchain and Machine Learning for IoT Security”, “IoT and Smart Devices for Sustainable Environment”, “Advanced Technology for Smart Environment and Energy”, and so ones. Finally, he has served as guest editor in journals “EAI Endorsed Transactions on Internet of Things”, “Tsinghua Science and Technology”, “Applied Sciences MDPI” and “Sustainability MDPI”Prof. Dr. Abdulatif Alabdulatif is an assistant professor at the School of Computer Science & IT, Qassim University, Saudi Arabia. He completed his Ph.D. degree in Computer Science from RMIT University, Australia in 2018. He received his B.Sc. degree in Computer Science from Qassim University, Saudi Arabia in 2008 and his M.Sc. degree in Computer Science from RMIT University, Australia in 2013. He has published more than 70 academic papers in prominent journals. His research interests include applied cryptography, cloud computing, and E-health.

Innehållsförteckning

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.