Anomaly detection in industrial control systems faces challenges such as lack of label information,class imbalance,and class overlap,which hinder existing classifiers from accurately detecting anomalies.Current data-level sampling methods suffer from inaccurate pseudo-labeling,poor sampling stability,and low overlap detection rates.Therefore,this paper pro-posed an undersampling method based on semi-supervised learning(SSLU-LP).This method combined the label propagation mechanism with a single class classifier through heterogeneous integration to supplement pseudo-labels.It constructed an over-lap region detection model using the minimum spanning tree strategy and employed an undersampling strategy to selectively re-move some majority class samples via nearest neighbor search.Finally,this paper combined the proposed method with 4 classi-cal classifiers and compared it with 9 hybrid algorithms on 9 industrial control datasets.Experimental results show that the pro-posed method can accurately pseudo-label unlabeled data,efficiently and effectively detect overlapping data in unbalanced datasets,improve the classifier's training performance,and enhance its anomaly detection capabilities.
关键词
工业控制系统/类不平衡/类重叠/半监督学习/异常检测
Key words
industrial control system/class imbalance/class overlap/semi-supervised learning/anomaly detection
维普
万方数据