Weakly-supervised IDS with abnormal-preserving transformation learning
Network intrusion detection systems are crucial for maintaining network security,and there is currently limited research on intrusion detection scenarios with only a few abnormal markers of network data.This paper designs a weakly-supervised learning intrusion detection model,called WIDS-APL,based on the anomaly retention of data.The detection model consists of four parts:data transfor-mation layer,representation learning layer,transformation classification layer,and anomaly discrimina-tion layer.By using a set of learnable encoders to map samples to different regions and compress them into a hypersphere,the label information of abnormal samples is used to learn the classification bounda-ries of normal and abnormal samples,and the abnormal score of the samples is obtained.Testing the WIDS-APL system on four datasets demonstrates the effectiveness and robustness of the system,with improvements in the AUC-ROC values of 4.80％,5.96％,1.58％,and 1.73％respectively compared to other mainstream methods.Furthermore,there are enhancements of 15.03％,2.95％,4.71％,and 9.23％in AUC-PR performance.
万方数据
维普
