TrafficMoE: Heterogeneity-aware Mixture of Experts for Encrypted Traffic Classification
Encrypted traffic classification is a critical task for network security. While deep learning has advanced this field, the occlusion of payload semantics by encryption severely challenges standard modeling approaches. Most existing frameworks rely on static and homogeneous pipelines that apply uniform parameter sharing and static fusion strategies across all inputs. This one-size-fits-all static design is inherently flawed: by forcing structured headers and randomized payloads into a unified processing pipeline, it inevitably entangles the raw protocol signals with stochastic encryption noise, thereby degrading the fine-grained discriminative features. In this paper, we propose TrafficMoE...
| Authors | Qing He, Xiaowei Fu, Lei Zhang |
|---|---|
| Primary category | cs.CR |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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