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

Fengchun Liu, Sen Zhang, Weining Ma and Jingguo Qu
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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
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
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
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

Mathematics, 2022, vol. 10, issue 15, 1-14

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.

Keywords: attack detection; cyber physical systems; data imbalance; principal component analysis; particle swarm optimization; support vector machine (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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