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The Internet of Things (IoT) includes a growing network of physical objects. The objects connect to the internet. They include things like household appliances, medical tools, and industrial equipment. More devices are being connected now. This has led to a greater emphasis on IT security. It calls for stronger measures to protect against cyber threats. Machine learning (ML) powered intrusion detection systems pinpoint weaknesses through precise vulnerability detection. This study examines the current state of intrusion detection in IoT networks by leveraging ML techniques. We analyze current applications of IDS, including an investigation of fifty research studies from 2020 to 2024, along with the datasets, feature selection, data balancing techniques, and machine learning methods employed.

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Machine Learning Approaches for Intrusion Detection in IoT: Literature Review

  • 会议论文

  • 发表日期：

    2025年

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  • Baich Marwa,
  • Sael Nawal,
  • Hamim Touria

摘要

The Internet of Things (IoT) includes a growing network of physical objects. The objects connect to the internet. They include things like household appliances, medical tools, and industrial equipment. More devices are being connected now. This has led to a greater emphasis on IT security. It calls for stronger measures to protect against cyber threats. Machine learning (ML) powered intrusion detection systems pinpoint weaknesses through precise vulnerability detection. This study examines the current state of intrusion detection in IoT networks by leveraging ML techniques. We analyze current applications of IDS, including an investigation of fifty research studies from 2020 to 2024, along with the datasets, feature selection, data balancing techniques, and machine learning methods employed.