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AI for IoT and OT Security

In: Understanding AI in Cybersecurity and Secure AI

Author

Listed:
  • 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)

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.

Suggested Citation

  • Dilli Prasad Sharma & Arash Habibi Lashkari & Mahdi Daghmehchi Firoozjaei & Samaneh Mahdavifar & Pulei Xiong, 2025. "AI for IoT and OT Security," Progress in IS, in: Understanding AI in Cybersecurity and Secure AI, chapter 0, pages 113-134, Springer.
  • Handle: RePEc:spr:prochp:978-3-031-91524-6_7
    DOI: 10.1007/978-3-031-91524-6_7
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