AI for IoT and OT Security

Dilli Prasad Sharma (), Arash Habibi Lashkari (), Mahdi Daghmehchi Firoozjaei (), Samaneh Mahdavifar () and Pulei Xiong ()
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Dilli Prasad Sharma: University of Toronto
Arash Habibi Lashkari: York University
Mahdi Daghmehchi Firoozjaei: MacEwan University
Samaneh Mahdavifar: McGill University
Pulei Xiong: National Research Council of Canada

Chapter Chapter 7 in Understanding AI in Cybersecurity and Secure AI, 2025, pp 113-134 from Springer

Abstract: Abstract The Internet of Things (IoT) connects billions of devices, enabling real-time data collection, processing, and communication, but also introduces significant security and privacy challenges. This chapter explores the security vulnerabilities in IoT ecosystems, categorizing threats across hardware, network, and software layers, including Denial of Service (DoS), Man-in-the-Middle (MITM) attacks, malware infections, and advanced microarchitectural exploits like Rowhammer, Spectre, and Meltdown. Additionally, it examines Industrial IoT (IIoT) and Operational Technology (OT) security risks, highlighting the need for AI-driven security solutions such as anomaly detection, predictive maintenance, behavioral analysis, and automated response systems. However, adversaries are also leveraging AI-enhanced cyberattacks, including AI-powered malware, botnets, and data poisoning techniques, necessitating robust security automation and compliance frameworks. The chapter concludes by discussing emerging trends in AI-based IoT security, such as Edge AI, self-adaptive security mechanisms, and regulatory challenges, aiming to enhance the resilience and protection of IoT infrastructures in an increasingly interconnected world.

Date: 2025
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DOI: 10.1007/978-3-031-91524-6_7

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