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The rapid growth of new network technologies and the continuous development of interconnected computer devices have resulted in a considerable quantity of Internet of Things (IoT) devices has increased. Systems for detecting intrusions (IDSs), or IDSs, play a major role in network security for the IoT. Creating an IDS with optimal accuracy and minimal handling of false alarms is a difficult task. The MLP and RF represent one of the biggest developments in Machine Learning (ML) in recent years. This work proposes an ensemble classification model using a variety of techniques. RF is believed to produce the most results when compared to other algorithms.

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Using Machine Learning to Improve IoT Network Security

  • 章节

  • 发表日期：

    2025年

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  • Asma’a Bassam Alamareen,
  • Malak Hamad Al-mashagbeh,
  • Sara Abuasal,
  • Abla Suliman Hussein

摘要

The rapid growth of new network technologies and the continuous development of interconnected computer devices have resulted in a considerable quantity of Internet of Things (IoT) devices has increased. Systems for detecting intrusions (IDSs), or IDSs, play a major role in network security for the IoT. Creating an IDS with optimal accuracy and minimal handling of false alarms is a difficult task. The MLP and RF represent one of the biggest developments in Machine Learning (ML) in recent years. This work proposes an ensemble classification model using a variety of techniques. RF is believed to produce the most results when compared to other algorithms.