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Three-Branch Random Forest Intrusion Detection Model

Author

Listed:
  • Chunying Zhang

    (College of Science, North China University of Science and Technology, Tangshan 063210, China
    Key Laboratory of Data Science and Application of Hebei Province, Tangshan 063210, China)

  • Wenjie Wang

    (College of Science, North China University of Science and Technology, Tangshan 063210, China)

  • Lu Liu

    (College of Science, North China University of Science and Technology, Tangshan 063210, China
    Key Laboratory of Data Science and Application of Hebei Province, Tangshan 063210, China)

  • Jing Ren

    (College of Science, North China University of Science and Technology, Tangshan 063210, China)

  • Liya Wang

    (College of Science, North China University of Science and Technology, Tangshan 063210, China
    Key Laboratory of Data Science and Application of Hebei Province, Tangshan 063210, China)

Abstract

Network intrusion detection has the problems of large amounts of data, numerous attributes, and different levels of importance for each attribute in detection. However, in random forests, the detection results have large deviations due to the random selection of attributes. Therefore, aiming at the current problems, considering increasing the probability of essential features being selected, a network intrusion detection model based on three-way selected random forest (IDTSRF) is proposed, which integrates three decision branches and random forest. Firstly, according to the characteristics of attributes, it is proposed to evaluate the importance of attributes by combining decision boundary entropy, and using three decision rules to divide attributes; secondly, to keep the randomness of attributes, three attribute random selection rules based on attribute randomness are established, and a certain number of attributes are randomly selected from three candidate fields according to conditions; finally, the training sample set is formed by using autonomous sampling method to select samples and combining three randomly selected attribute sets randomly, and multiple decision trees are trained to form a random forest. The experimental results show that the model has high precision and recall.

Suggested Citation

  • Chunying Zhang & Wenjie Wang & Lu Liu & Jing Ren & Liya Wang, 2022. "Three-Branch Random Forest Intrusion Detection Model," Mathematics, MDPI, vol. 10(23), pages 1-21, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4460-:d:984909
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    References listed on IDEAS

    as
    1. Mahesh Kumar Prasath & Balasubramani Perumal, 2019. "A meta‐heuristic Bayesian network classification for intrusion detection," International Journal of Network Management, John Wiley & Sons, vol. 29(3), May.
    2. Musavir Hassan & Muheet Ahmed Butt & Majid Zaman, 2021. "An Ensemble Random Forest Algorithm for Privacy Preserving Distributed Medical Data Mining," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 12(6), pages 1-23, November.
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