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Clustering Detection Method of Network Intrusion Feature Based on Support Vector Machine and LCA Block Algorithm

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Abstract

Traditional methods ignore the imbalance of network data, resulting in unsatisfactory clustering detection results, long detection time, and high rate of missed detection and false alarm. In this regard, this paper proposes a clustering detection method of network intrusion feature based on support vector machine and LCA block algorithm. Firstly, the useless features were deleted by reducing the dimension of the data set, thus improving the clustering detection accuracy. Secondly, the training sample set was divided, and the multi-level support vector model was established by two classification support vector machines. Finally, the LCA algorithm was adopted to identify the network intrusion features, achieving clustering detection of network intrusion feature. The results show that the proposed method achieves better clustering detection results and effectively reduces the average clustering detection time.

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Acknowledgements

The research is supported by 2017 National Natural Science Foundation of China Youth Foundation Project :(2018.1~2020.12)" Research on Multi-scale Analysis and Precision Diagnostic methods of Breast Pathological Image based on Deep Learning "Approval No .61702026;Henan Province 2018 Science and Technology Research Project-the key Technology Research of big data Analysis platform for developing Intelligent Agricultural batches in distributed heterogeneous Environment, Project No .182102110277

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Liaoning Technical University, Fuxin, 125000, China

    Jie Zhang & Jinguang Sun

  2. Department of Mechanical Engineering, TsingHua University, Beijing, 100084, China

    Hua He

Authors
  1. Jie Zhang
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  2. Jinguang Sun
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  3. Hua He
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Correspondence to Jie Zhang.

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Zhang, J., Sun, J. & He, H. Clustering Detection Method of Network Intrusion Feature Based on Support Vector Machine and LCA Block Algorithm. Wireless Pers Commun 127, 599–613 (2022). https://doi.org/10.1007/s11277-021-08353-y

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