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The development and enhancement of long-distance communication techniques on the Internet has led to security issues. Computer intrusions and attacks can be detected using an IDS. The accuracy of intrusion detection and prevention systems has increased through the use of various ML approaches. According to this study, real time scenarios with Network Simulator2 (NS2) is created and PCA is used for dimensionality. By reducing the size, PCA makes it easier to regularize the statistics, which supports the arbitrary distribution of random forest. NS2 is used to model the behaviour in a dispersed IoT environment in order to produce reliable information for IDS. The proposed method has 96.78% accuracy, 0.21% loss and is completely compatible with ML and NS2.

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Intrusion Detection System in Distributed Networks in IoT Using Machine Learning and Network Simulator 2

  • 会议论文

  • 发表日期：

    2025年

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  • A. Parveen Akhther,
  • G. Sri Vasanthi,
  • Uyyala Bindu,
  • V. S. Prasanth

摘要

The development and enhancement of long-distance communication techniques on the Internet has led to security issues. Computer intrusions and attacks can be detected using an IDS. The accuracy of intrusion detection and prevention systems has increased through the use of various ML approaches. According to this study, real time scenarios with Network Simulator2 (NS2) is created and PCA is used for dimensionality. By reducing the size, PCA makes it easier to regularize the statistics, which supports the arbitrary distribution of random forest. NS2 is used to model the behaviour in a dispersed IoT environment in order to produce reliable information for IDS. The proposed method has 96.78% accuracy, 0.21% loss and is completely compatible with ML and NS2.