Time series intrusion warning with GAN for missing data in CPS

Time series intrusion warning with GAN for missing data in CPS

연구 분야: Infrastructure

학회: ICCBN '23: Proceedings of the 2023 11th International Conference on Communications and Broadband Networking

Attacks on Cyber-Physical Systems (CPS) are extremely costly to repair, which is why the early warning is critical for industrial control system security. Previous warning methods have relied heavily on statistical analysis, but such methods struggle to adequately capture the nonlinear relationship between time series data and describe its changing trend. It has limitations in the face of long-term forecasts. This paper analyses the main characteristics of intrusion data in CPS: high dimensionality, data imbalance, and missing data due to few failure samples and sensor failures in highly reliable equipment. Based on the above problem, we propose an intrusion warning system based on missing data, which consists of three modules: data collection module, data interpolation module and intrusion warning module. The improved Generative Adversarial Networks (GAN) model is used to interpolate the missing data and improve the quality of the interpolated data, which is then handed over to the downstream model for intrusion warning. Experiments are conducted on two intrusion datasets collected from CPS. The experimental results show that the model produces more accurate interpolated data and has a high detection rate in intrusion warning work. The proposed data repair and warning method are more applicable to CPS.