Title: S$^2$-ETC: Semantic-Structure Aware Encrypted Traffic Classification via Hyper-Bipartite Graph
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Encrypted Traffic Classification, Hypergraph Learning, Bipartite Graph

Abstract: In the era of pervasive encryption, encrypted traffic classification serves as a fundamental technique, underpinning diverse applications including intrusion detection and network management. It is commonly approached with deep learning and pretraining methods; however, these approaches suffer from three major limitations: overfitting to semantic features with poor generalization, heavy reliance on prior knowledge with weak cross-domain robustness, and constrained scalability to large and complex traffic datasets. To address these challenges, we propose S$^2$-ETR (Semantic-Structure Encrypted Traffic Representation), a novel framework that integrates traffic semantics with communication topology graph. The framework includes a Hyper-Bipartite Graph (HBG), which takes two branches to fuse topology and semantic features. The topology branch models structural relations with an IP–flow bipartite graph, decoupling flows from communication entities to mitigate overfitting. The semantic branch employs a lightweight adapter to capture flow semantics, enhancing cross-domain robustness; meanwhile, it constructs semantic hyperedges via implicit hypergraph learning, propagating global semantic representations without extra information. Finally, a conditional probability–based hierarchical classification strategy is introduced to augment scalability on massive traffic graphs. Furthermore, through a mathematical proof, we demonstrate that HBG reduces long-range dependencies and over-squashing, leading to better efficacy and generalization compared to traditional topology graphs. Experimental results show that S$^2$-ETR consistently achieves state-of-the-art performance across 4 datasets of varying scales, outperforming 11 baselines by 2.4%–17.1% on encrypted application classification datasets, and surpassing the best baseline by 9.2% on the more complex and challenging IoT dataset.