Enhanced anomaly traffic detection framework using BiGAN and contrastive learning

Abstract

Abstract Abnormal traffic detection is a crucial topic in the field of network security. However, existing methods face many challenges when processing complex high-dimensional traffic data. Especially in dealing with redundant features, data sparsity and nonlinear features, traditional methods often suffer from high computational complexity and low detection efficiency. It is challenging to capture potential patterns in complex data effectively and cannot fully meet the needs of practical applications. To address these challenges, this paper proposes an enhanced anomaly traffic detection framework using bidirectional generative adversarial networks (BiGAN) and contrastive learning. This method preprocesses high-dimensional data through steps such as data cleaning, normalization, and clustering to improve data quality. It uses BiGAN and contrastive learning technology to enhance the model's feature representation capabilities. Experimental results show that the method proposed in this paper performs well on multiple traffic data sets and significantly improves the accuracy and efficiency of anomaly detection. Overall, the solution proposed in this paper effectively overcomes the limitations of existing methods in high-dimensional data processing and provides a more advanced abnormal traffic detection strategy.

Keywords

• Cyber security
• Network traffic
• Anomaly detection