Flow Transformer: A Novel Anonymity Network Traffic Classifier with Attention Mechanism

Supervising anonymity network is a critical issue in the field of network security, and traditional traffic analysis methods cannot cope with complex anonymity traffic. In recent years, the traffic analysis method based on deep learning has achieved good performance. However, most of the existing studies do not consider the temporal-spatial correlation of the traffic, and only use a single flow for classification. A few works take continuous flows as flow sequence for traffic classification, but they do not distinguish the different importance of each flow. To tackle this issue, we propose a novel flow-based traffic classifier called FLOW TRANSFORMER to classify anonymity network traffic. FLOW TRANSFORMER uses multi-head attention mechanism to set higher weights for important flows, and extracts flow sequence features according to the importance weights. Besides, the RF-based feature selection method is designed to select the optimal feature combination, which can effectively avoid the insignificant features from reducing the performance and efficiency of the classifier. Experimental results on two real-world traffic datasets demonstrate that the proposed method outperforms state-of-the-art methods with a large margin.

Automated summary

Supervising anonymity network is crucial in network security field. Traditional traffic analysis methods are ineffective against complex anonymity traffic. Deep learning-based traffic analysis methods have proven effective, but many studies overlook temporal-spatial correlation of traffic. The proposed FLOW TRANSFORMER classifier effectively handles anonymity traffic by utilizing multi-head attention mechanism and RF-based feature selection, outperforming existing methods significantly in real-world datasets.