Cross-Domain Industrial Intrusion Detection Deep Model Trained With Imbalanced Data
Yongle Chen, Sida Su, Dan Yu, Hao He, Xiaojian Wang, Yao Ma, Hao Guo
Abstract
Constrained by the high acquisition and labeling cost, traffic data in industrial control systems (ICSs) are usually extremely imbalanced. Deep-learning-based (DL) industrial control intrusion detection systems (IDSs) are not applicable to dynamic networks and show a limited detection performance. In this article, we enhanced the information transmission link in adversarial domain adaptation (DA) and proposed an information-enhanced adversarial DA (IADA) method. Our method could train a cross-domain industrial intrusion detection deep model with imbalanced data and maintained high detection accuracy. The experimental results based on SCADA network layer data showed that the detection accuracy of the gated recurrent unit model trained in IADA reached 93.7% and 91.3% in the two transfer tasks with a significant cross-domain discrepancy.