Visible to the public Application of Optimized Bidirectional Generative Adversarial Network in ICS Intrusion Detection

TitleApplication of Optimized Bidirectional Generative Adversarial Network in ICS Intrusion Detection
Publication TypeConference Paper
Year of Publication2020
AuthorsLiu, H., Zhou, Z., Zhang, M.
Conference Name2020 Chinese Control And Decision Conference (CCDC)
KeywordsBiGAN, Computational modeling, control engineering computing, control system security, data acquisition, generative adversarial networks, high-dimensional network traffic data, ICS intrusion detection method, industrial control, industrial control system, industrial control systems, integrated circuits, Intrusion detection, massive network traffic data, network intrusion detection, neural nets, optimal model, optimisation, Optimization, optimized bidirectional generative adversarial network, optimized BiGAN, parameter optimization, pubcrawl, resilience, Resiliency, SCADA, SCADA systems, Scalability, security of data, single variable principle, supervised control, Training

Aiming at the problem that the traditional intrusion detection method can not effectively deal with the massive and high-dimensional network traffic data of industrial control system (ICS), an ICS intrusion detection strategy based on bidirectional generative adversarial network (BiGAN) is proposed in this paper. In order to improve the applicability of BiGAN model in ICS intrusion detection, the optimal model was obtained through the single variable principle and cross-validation. On this basis, the supervised control and data acquisition (SCADA) standard data set is used for comparative experiments to verify the performance of the optimized model on ICS intrusion detection. The results show that the ICS intrusion detection method based on optimized BiGAN has higher accuracy and shorter detection time than other methods.

Citation Keyliu_application_2020