AN-Net: an Anti-Noise Network for Anonymous Traffic Classification

Anonymous networks employ a triple proxy to transmit packets to enhance user privacy, causing traffic packets from all applications and web services to form a unified flow. The traditional approach of applying flow-level encrypted traffic classification methods to anonymous traffic (i.e., treating consecutive packets as a single flow) is hindered by irrelevant packet noise. Moreover, fluctuations in the network environment can introduce per-packet attribute noise and discrepancies between training and test data. How to extract robust patterns from consecutive packets replete with noise remains a key challenge. In this paper, we propose the Anti-Noise Network (AN-Net) to construct robust short-term representations for a single modality, effectively countering irrelevant packet noise. We also incorporate an enhanced multi-modal fusion approach to combat per-packet attribute noise. AN-Net achieves state-of-the-art performance across two anonymous traffic classification tasks and one VPN traffic classification task, notably elevating the F1 score of SJTU-AN21 to 94.39% (6.24%↑). Our code and dataset are available on https://github.com/SJTU-dxw/AN-Net.