Abstract
As an important part of guard system, intrusion detection systems have great significance to the security of industrial control systems. Cause the industrial control systems of the on-site environment is often very complicated, the process data extracted on-site have a wide variety of features, and they always have complex associations and are not independent. The intrusion detection algorithms for traditional industrial control systems often do not solve this problem, which results in a decrease in the accuracy, precision and high false negative rate (FNR), false positives rate (FPR). Aiming at solving the current problems, this paper proposes an algorithm combining the Fast Independent Principal Component Analysis (FastICA) and Support Vector Machine (SVM). Finally compared with the SVM and PCA-SVM algorithms, the experimental results show that the accuracy rate has been significantly improved and slight FPR.
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Funding
This work is supported in part by “National Key R&D Program of China” (2019YFB2006300), Shenyang Science and Technology Development [2019] No. 66 (Z191001).
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Chen, H., Liu, X., Wang, T., Zhang, X. (2021). Research on Intrusion Detection of Industrial Control System Based on FastICA-SVM Method. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_26
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DOI: https://doi.org/10.1007/978-3-030-78609-0_26
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