Network traffic anomaly detection driven by bidirectional self-attention mechanism

In the face of increasingly covert cyber-attacks, traditional detection models struggle to effectively capture the complex contextual correlation features in the traffic, resulting in insufficient ability to identify new threats.To address this issue, this study proposes a detection model based on bidirectional self-attention mechanism, which achieves deep perception of abnormal behaviours by simultaneously learning the context information of the traffic sequence.Experimental results show that compared with mainstream long short-term memory and standard transformer methods, this model has an average area under the curve improvement of over 4.2%, and the recall rate for low-rate attacks has increased by 7.5%, significantly enhancing the accuracy and robustness of detection.This study provides a new idea for improving the active defence capability of network security.