What question did this study set out to answer?

The research aims to enhance anomaly detection in network traffic by leveraging a bidirectional self-attention mechanism.

May 24, 2026International Journal of Information and Communication Technology0 citationsOpen Access

Network traffic anomaly detection driven by bidirectional self-attention mechanism

JLJing Luo JSJie Song

Key Points

The research aims to enhance anomaly detection in network traffic by leveraging a bidirectional self-attention mechanism.
Developed a detection model based on bidirectional self-attention mechanism to analyze traffic sequence.
Compared performance with long short-term memory and standard transformer models in terms of detection accuracy.
Measured improvements using area under the curve and recall rates for low-rate attacks.
Achieved an average area under the curve improvement of over 4.2%.
Increased recall rate for low-rate attacks by 7.5%.
Significantly enhanced accuracy and robustness in detecting anomalies.

Abstract

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.

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