AN-Net: An Anti-Noise Network For Anonymous Traffic Classification

Xianwen Deng, Yijun Wang, Zhi Xue

Keywords: Anonymous Traffic Classification, Irrelevant Packet Noise, Per-Packet Attribute Noise, Short-Term Representation, Multi-Modal Fusion

Abstract:

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\%$\uparrow$). In particular, attackers cannot easily disrupt all short-term features of all modalities and thus AN-Net is robust against injected noise packet attacks. Our codes will be available on GitHub after the double-blind review process.

Track: Web Mining and Content Analysis

Submission Guidelines Scope: Yes

Submission Guidelines Blind: Yes

Submission Guidelines Format: Yes

Submission Guidelines Limit: Yes

Submission Guidelines Authorship: Yes

Student Author: Yes

Submission Number: 2321