Cross-domain Anomaly Detection for Power Industrial Control System
| Title | Cross-domain Anomaly Detection for Power Industrial Control System |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Li, Y., Ji, X., Li, C., Xu, X., Yan, W., Yan, X., Chen, Y., Xu, W. |
| Conference Name | 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC) |
| Date Published | July 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-6313-0 |
| Keywords | anomaly detection, feature extraction, ICS Anomaly Detection, industrial control, integrated circuits, Neural networks, Power ICS, Protocols, pubcrawl, resilience, Resiliency, Scalability, TrAdaBoost, Training data, transfer learning |
| Abstract | 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. |
| URL | https://ieeexplore.ieee.org/document/9152334 |
| DOI | 10.1109/ICEIEC49280.2020.9152334 |
| Citation Key | li_cross-domain_2020 |