Cross-domain Anomaly Detection for Power Industrial Control System
Yanjie Li, Xiaoyu Ji, Chenggang Li, Xiaofeng Xu, Wei Qi Yan, Xu Yan, Yanjiao Chen, Wenyuan Xu · 2020
In recent years, artificial intelligence has been widely used in the field of network security, which has significantly improved the effect of network security analysis and detection. However, because the power industrial control system is faced with the problem of shortage of attack data, the direct deployment of the network intrusion detection system based on artificial intelligence is faced with the problems of lack of data, low precision, and high false alarm rate. To solve this problem, we propose an anomaly traffic detection method based on cross-domain knowledge transferring. By using the TrAdaBoost algorithm, we achieve a lower error rate than using LSTM alone.