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, a framework that breaks through the bottleneck of static modeling by establishing a Disentangle-Filter-Aggregate (DFA) paradigm. Specifically, to resolve the structural between-components conflict, the architecture disentangles headers and payloads using dual-branch sparse Mixture-of-Experts (MoE), enabling modality-specific modeling. To mitigate the impact of stochastic noise, an uncertainty-aware filtering mechanism is introduced to quantify reliability and selectively suppress high-variance representations. Finally, to overcome the limitations of static fusion, a routing-guided strategy aggregates cross-modality features dynamically, that adaptively weighs contributions based on traffic context. With this DFA paradigm, TrafficMoE maximizes representational efficiency by focusing solely on the most discriminative traffic features. Extensive experiments on six datasets demonstrate TrafficMoE consistently outperforms state-of-the-art methods, validating the necessity of heterogeneity-aware modeling in encrypted traffic analysis. The source code is publicly available at https://github.com/Posuly/TrafficMoE_main.

翻译：加密流量分类是网络安全中的一项关键任务。尽管深度学习推动了该领域的发展，但加密过程对负载语义的遮蔽严重挑战了标准建模方法。现有框架大多依赖静态且同质化的流水线，对所有输入施加统一的参数共享策略与静态融合机制。这种“一刀切”的静态设计存在固有缺陷：通过将结构化的头部与随机化的负载强制纳入统一处理流程，它不可避免地使原始协议信号与随机加密噪声相互纠缠，从而削弱细粒度判别特征。本文提出TrafficMoE框架，通过构建“解耦-过滤-聚合”（DFA）范式突破静态建模瓶颈。具体而言，为解决组件间的结构性冲突，该架构利用双分支稀疏混合专家模型（MoE）对头部和负载进行解耦，实现模态特定建模。为缓解随机噪声的影响，引入不确定性感知过滤机制以量化可靠性并选择性抑制高方差表征。最后，为克服静态融合的限制，路由引导策略动态聚合跨模态特征，根据流量上下文自适应地权衡各模态贡献。通过这一DFA范式，TrafficMoE仅聚焦最具判别性的流量特征，最大化表征效率。在六个数据集上的大量实验表明，TrafficMoE始终优于现有最优方法，验证了加密流量分析中异质性感知建模的必要性。源代码已在https://github.com/Posuly/TrafficMoE_main 公开。