AI and ML in IoT Security
Challenges, Solutions, and Future Directions
1st Edition
Will be published approx. on 30. September 2026
Book
Hardback
384 pages
978-1-041-22763-2 (ISBN)
Description
The Internet of Things (IoT) has emerged as a fundamental component of contemporary digital ecosystems, facilitating extensive connectivity across sectors like healthcare, smart cities, transportation, industrial automation, and critical infrastructure. While this widespread interconnectivity has brought substantial advantages, it has also increased the risk of cyberattacks, which leaves IoT systems vulnerable to a wide range of complex security threats. The diversity of devices, reliance on open communication channels, and limited resources exacerbate these vulnerabilities, making traditional rule-based security approaches inadequate for addressing modern challenges.
AI and ML in IoT Security: Challenges, Solutions, and Future Directions explores how these smart technologies are critical in securing IoT systems. It explains how they can be used to analyze vast amounts of data, detect anomalies, and respond to evolving threats in real time. It also explores how:
TinyML enables intelligent, autonomous defense directly on constrained IoT devices
Explainable AI can enhance transparency, trust, and human-machine collaboration in protecting critical IoT-enabled infrastructure
Integrating deep learning, NLP, reinforcement learning, and SOAR systems demonstrates scalable and explainable intrusion detection across IoT, cloud, and edge environments
Ensemble learning can achieve accurate and timely detection with acceptable computational overhead.
Providing a comprehensive and forward-looking perspective on securing IoT ecosystems using AI and ML, the book is a critical reference for researchers, practitioners, graduate students, and industry professionals seeking to design intelligent, resilient, and privacy-aware IoT security solutions.
AI and ML in IoT Security: Challenges, Solutions, and Future Directions explores how these smart technologies are critical in securing IoT systems. It explains how they can be used to analyze vast amounts of data, detect anomalies, and respond to evolving threats in real time. It also explores how:
TinyML enables intelligent, autonomous defense directly on constrained IoT devices
Explainable AI can enhance transparency, trust, and human-machine collaboration in protecting critical IoT-enabled infrastructure
Integrating deep learning, NLP, reinforcement learning, and SOAR systems demonstrates scalable and explainable intrusion detection across IoT, cloud, and edge environments
Ensemble learning can achieve accurate and timely detection with acceptable computational overhead.
Providing a comprehensive and forward-looking perspective on securing IoT ecosystems using AI and ML, the book is a critical reference for researchers, practitioners, graduate students, and industry professionals seeking to design intelligent, resilient, and privacy-aware IoT security solutions.
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Academic
Illustrations
40 s/w Tabellen, 80 s/w Zeichnungen, 80 s/w Abbildungen
40 Tables, black and white; 80 Line drawings, black and white; 80 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
ISBN-13
978-1-041-22763-2 (9781041227632)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Debabrata Samanta | Subir Panja
AI and ML in IoT Security
Challenges, Solutions, and Future Directions
E-Book
approx. 09/2026
Auerbach
€68.49
Not yet available
Debabrata Samanta | Subir Panja
AI and ML in IoT Security
Challenges, Solutions, and Future Directions
E-Book
approx. 09/2026
Auerbach
€68.49
Not yet available
Persons
Dr. Debabrata Samanta serves as the Department Chair and Assistant Professor in the Department of Computing and Information Technologies at the American Academy of Technology Tirana (commonly known as Rochester Institute of Technology - Tirana), Romania. Specializing in SAR Image Processing, he earned his Ph.D. in Computer Science and Engineering from the National Institute of Technology, Durgapur, India. Dr. Samanta has a strong interest in interdisciplinary research and development. His expertise spans various fields, including SAR Image Analysis, Video Surveillance, Heuristic Algorithms for Image Classification, Deep Learning Frameworks for Detection and Classification, Blockchain, Statistical Modeling, Wireless Adhoc Networks, and Natural Language Processing.
Dr. Subir Panja is an Associate Professor and Head of the Department of Computer Science and Engineering (Cyber Security) at Dr. B.C. Roy Engineering College, Durgapur, West Bengal, India. He earned his Ph.D. in Computer Science and Engineering from the Central Institute of Technology (CIT), Kokrajhar, where his research focused on enhancing the security of the Internet of Medical Things (IoMT) using deep learning techniques. With over 20 years of academic, teaching, and research experience, Dr. Panja has served in various capacities, including Lecturer and Assistant Professor, at reputed engineering institutions. His research interests encompass IoT Security, Cyber Security, Machine Learning, Deep Learning, Anomaly Detection, Multimedia Systems, and Intelligent Healthcare Applications. Dr. Panja has authored and edited several books and book chapters, published numerous research papers in reputed journals and conferences, and actively contributes to research and academic development in the fields of cybersecurity and artificial intelligence. He is committed to advancing innovative and interdisciplinary research that addresses emerging challenges in secure and intelligent computing systems.
Dr. Subir Panja is an Associate Professor and Head of the Department of Computer Science and Engineering (Cyber Security) at Dr. B.C. Roy Engineering College, Durgapur, West Bengal, India. He earned his Ph.D. in Computer Science and Engineering from the Central Institute of Technology (CIT), Kokrajhar, where his research focused on enhancing the security of the Internet of Medical Things (IoMT) using deep learning techniques. With over 20 years of academic, teaching, and research experience, Dr. Panja has served in various capacities, including Lecturer and Assistant Professor, at reputed engineering institutions. His research interests encompass IoT Security, Cyber Security, Machine Learning, Deep Learning, Anomaly Detection, Multimedia Systems, and Intelligent Healthcare Applications. Dr. Panja has authored and edited several books and book chapters, published numerous research papers in reputed journals and conferences, and actively contributes to research and academic development in the fields of cybersecurity and artificial intelligence. He is committed to advancing innovative and interdisciplinary research that addresses emerging challenges in secure and intelligent computing systems.
Content
1. Deep Dive into Deep Learning for IoT Protection 2. A Systematic Review of Security in the Context of Cloud Authentication and Authorization Schemes 3. Emergence of Sinkhole Attack and LDoS Attack in IoT Environments 4. Explainable AI for Detecting and Understanding Cyberattacks on IoT-Enabled Critical Infrastructure: A Case Study on European Airports 5. Machine Learning for DDoS Attack Detection in IoT 6. Outsmarting the Hackers: Battling Adversarial Attacks 7. Machine Learning and Deep Learning for Next-Generation Intrusion Detection Systems 8. AI-Enhanced Intrusion Detection: The Next-Gen Shield 9. Stronger Together: Hybrid Security Models for IoT 10. Adversarial Machine Learning for IoT Security: Attacks, Mathematical Foundations, and Robust Defenses 11. TinyML in IoT Security: On-Device Anomaly Detection on Resource-Limited Microcontrollers 12. Words as Weapons: NLP for IoT Security 13. Edge-Based Federated Learning in IoT: Architectures, Security, and Challenges 14. Privacy at the Edge: Federated Learning for IoT 15. Edge-Centric Federated Learning for IoT: Balancing Privacy, Efficiency, and Scalability