Del i serien Advances in Computational Collective Intelligence

AI and ML in IoT Security

Nyhet

Challenges, Solutions, and Future Directions

Inbunden, Engelska, 2026

AvDebabrata Samanta,Subir Panja

2 089 kr

Kommande

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 devicesExplainable AI can enhance transparency, trust, and human–machine collaboration in protecting critical IoT-enabled infrastructureIntegrating deep learning, NLP, reinforcement learning, and SOAR systems demonstrates scalable and explainable intrusion detection across IoT, cloud, and edge environmentsEnsemble 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.

Produktinformation

Utgivningsdatum2026-09-01
Mått156 x 234 x undefined mm
FormatInbunden
SpråkEngelska
SerieAdvances in Computational Collective Intelligence
Antal sidor384
FörlagTaylor & Francis Ltd
ISBN9781041227632

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Elektronik och kommunikationer inom Naturvetenskap och teknik
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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 in the Department of Computer Science and Engineering at the Academy of Technology, Adisaptagram, West Bengal, India. With a focus on enhancing security using deep learning for the Internet of Medical Things (IoMT, he earned his Ph.D. in Computer Science and Engineering from the Central Institute of Technology, Kokrajhar. With more than two decades of teaching and research experience, he has previously held positions as Lecturer and Assistant Professor at leading engineering institutions. Dr. Panja’s research interests include IoT security, machine learning, deep learning, anomaly detection, and multimedia systems.

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