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AI and ML in IoT Security
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AI and ML in IoT Security : Challenges, Solutions, and Future Directions

Book Details

Format Hardback or Cased Book
ISBN-10 1041227639
ISBN-13 9781041227632
Publisher Taylor & Francis Ltd
Imprint Auerbach
Country of Manufacture GB
Country of Publication GB
Publication Date Nov 6th, 2026
Print length 384 Pages
Ksh 24,300.00
Not Yet Published 0 in stock

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The book addresses the intersection of the Internet of Things (IoT) with advanced artificial intelligence (AI) and machine learning (ML) technologies. It explores the utilization of AI and ML to create adaptive, intelligent systems that facilitate real-time detection, anomaly identification, and privacy protection.
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.

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