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Research on Attack Detection of Cyber Physical Systems Based on Improved Support Vector Machine

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  • Fengchun Liu

    (Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China
    Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China
    Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan 063210, China
    The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China)

  • Sen Zhang

    (Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China
    Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China
    Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan 063210, China
    The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China)

  • Weining Ma

    (Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China
    Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China
    Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan 063210, China
    The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China)

  • Jingguo Qu

    (Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China
    Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China
    Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan 063210, China
    The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China)

Abstract

Cyber physical systems (CPS), in the event of a cyber attack, can have a serious impact on the operating physical equipment. In order to improve the attack detection capability of CPS, an support vector machine (SVM) attacks detection model based on particle swarm optimization (PSO) is proposed. First, the box plot anomaly detection method is used to detect the characteristic variables, and the characteristic variables with abnormal distribution are discretized. Secondly, the number of attack samples was increased by the SMOTE method to solve the problem of data imbalance, and the linear combination of characteristic variables was performed on the high-dimensional CPS network traffic data using principal component analysis (PCA). Then, the penalty coefficient and the hyperparameter of the kernel function in the SVM model are optimized by the PSO algorithm. Finally, Experiments on attack detection of CPS network traffic data show that the proposed model can detect different types of attack data and has higher detection accuracy compared with general detection models.

Suggested Citation

  • Fengchun Liu & Sen Zhang & Weining Ma & Jingguo Qu, 2022. "Research on Attack Detection of Cyber Physical Systems Based on Improved Support Vector Machine," Mathematics, MDPI, vol. 10(15), pages 1-14, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2713-:d:877548
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    References listed on IDEAS

    as
    1. Carlos Martin-Barreiro & John A. Ramirez-Figueroa & Xavier Cabezas & Victor Leiva & Ana Martin-Casado & M. Purificación Galindo-Villardón, 2021. "A New Algorithm for Computing Disjoint Orthogonal Components in the Parallel Factor Analysis Model with Simulations and Applications to Real-World Data," Mathematics, MDPI, vol. 9(17), pages 1-22, August.
    2. Liyan Zhang & Jingfeng Guo & Jiazheng Wang & Jing Wang & Shanshan Li & Chunying Zhang, 2022. "Hypergraph and Uncertain Hypergraph Representation Learning Theory and Methods," Mathematics, MDPI, vol. 10(11), pages 1-22, June.
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