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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.