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As the Internet of Things (IoT) continues to expand, ensuring the security of these devices is essential. Intrusion detection systems (IDS) play a crucial role in protecting IoT devices from malicious attacks. This review explores the machine learning techniques employed in IDS for IoT devices. It classifies these technologies based on their applicability, benefits, and limitations, highlighting their effectiveness in addressing specific challenges within the IoT landscape. In addition, the paper discusses future directions for managing ML-based IDS in the IoT domain.

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Machine Learning Techniques for Intrusion Detection System in IOT Devices: A Review

  • 会议论文

  • 发表日期：

    2026年

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  • Navodita Singh,
  • Akhtar Rasool,
  • Pragati Agrawal,
  • Saima Hasib

摘要

As the Internet of Things (IoT) continues to expand, ensuring the security of these devices is essential. Intrusion detection systems (IDS) play a crucial role in protecting IoT devices from malicious attacks. This review explores the machine learning techniques employed in IDS for IoT devices. It classifies these technologies based on their applicability, benefits, and limitations, highlighting their effectiveness in addressing specific challenges within the IoT landscape. In addition, the paper discusses future directions for managing ML-based IDS in the IoT domain.