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Research on Network Intrusion Detection Technology Based on Machine Learning

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Abstract

Aiming at the problems of low accuracy and efficiency and high false alarm rate in current network intrusion detection methods, a network intrusion detection method based on machine learning is proposed. Machine learning algorithm is used to classify network packets, and information gain is used as attribute selection measure to train and test multiple samples. Based on the principle of structural risk minimization, the optimal interval is obtained and the difference function is obtained. The intrusion detection framework is constructed by collecting network traffic data, system logs, user behavior information and host information. This paper establishes the evaluation index of machine learning network intrusion detection, analyzes and constructs the machine learning network intrusion detection model, preprocesses the data of intrusion detection model, uses random forest algorithm to learn and train the data, calculates the importance of features, reduces the data dimension, and realizes network intrusion detection. The experimental results show that this method has high accuracy and efficiency, and can effectively reduce the false alarm rate of network intrusion detection.

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All data, models, and code generated or used during the study appear in the submitted article.

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Authors and Affiliations

  1. State Grid Fujian Electric Power Company, Fuzhou, 350000, China

    Fei Wu, Ting Li, Zhen Wu, ShuLin Wu & ChuanQi Xiao

Authors
  1. Fei Wu
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  2. Ting Li
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  3. Zhen Wu
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  4. ShuLin Wu
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  5. ChuanQi Xiao
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Correspondence to Fei Wu.

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Wu, F., Li, T., Wu, Z. et al. Research on Network Intrusion Detection Technology Based on Machine Learning. Int J Wireless Inf Networks 28, 262–275 (2021). https://doi.org/10.1007/s10776-021-00520-z

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  • DOI: https://doi.org/10.1007/s10776-021-00520-z

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