Research on intrusion detection method of industrial control system based on LightGBM-SVM

Linchang Fan; Jinqiang Ma; Huawei Wang

doi:10.1117/12.2640897

2 December 2022 Research on intrusion detection method of industrial control system based on LightGBM-SVM

Linchang Fan, Jinqiang Ma, Huawei Wang

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 122881O (2022) https://doi.org/10.1117/12.2640897
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

With the continuous development of information technology, industrial control systems have become an importantpart of the national information infrastructure, and the network security risks are further complicated. It’s imperative to use new technologies and methods to detect and defend against attacks.This paper constructs the LightBGM-SVM model, tests it combined with the experimental data of industrial control system, and compares the differences of three common intrusion detection models in detection rate, false alarm rate and response time.The verificationresults show that the LightBGMSVM model is significantly better than the other two models in the test set verification. The application of this model to the intrusion detection of industrial control system has better accuracy, and further defines the optimization direction of intrusion detection for more complex industrial control system.

Citation Download Citation

Linchang Fan, Jinqiang Ma, and Huawei Wang "Research on intrusion detection method of industrial control system based on LightGBM-SVM", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 122881O (2 December 2022); https://doi.org/10.1117/12.2640897

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