Deep learning-based website fingerprinting has emerged as an effective technique for inferring the websites users visit. Although existing methods achieve strong performance on closed-world datasets, they often fail to generalize to real-world environments, especially under geographic and temporal shifts. This limitation fundamentally stems from the coupled effects of two key challenges: application-layer resource composition variability and observable feature instability induced by cross-layer encapsulation. Intertwined, these factors induce systematic shifts between underlying application semantics and observable traffic features. To address the above challenges, we propose SATA , a semantics-aware traffic augmentation framework. Specifically, SATA first performs application-layer semantic augmentation based on protocol rules, expanding the resource composition patterns within each flow and frame sequence patterns under protocol constraints. Based on these augmented frame sequences, we further introduce a cross-layer feature alignment mechanism via knowledge distillation. It aligns frame sequence with packet-length sequence features, enabling cross-layer feature alignment between enhanced semantics and observable sequences. Extensive experiments show that SATA successfully generates traffic patterns that are absent from the training set but genuinely exist in the test set, and significantly improves the performance of mainstream models across diverse and complex scenarios. In particular, in open-world settings, SATA improves ACC by 90.81% and AUROC by 48.37%. The source code of the prototype system is available at https://anonymous.4open.science/r/SATA-B6C2/.

翻译：[translated abstract in Chinese] 基于深度学习的网站指纹识别已成为推断用户访问网站的有效技术。尽管现有方法在封闭世界数据集上表现出色，但它们通常难以泛化到真实环境，尤其是在地理和时间变化下。这一局限性从根本上源于两个关键挑战的耦合效应：应用层资源组成变异性以及跨层封装导致的可观测特征不稳定性。这些因素相互交织，在底层应用语义与可观测流量特征之间引起系统性偏移。为应对上述挑战，我们提出SATA，一种语义感知的流量增强框架。具体而言，SATA首先基于协议规则执行应用层语义增强，扩展每个流内的资源组成模式以及协议约束下的帧序列模式。基于这些增强的帧序列，我们进一步通过知识蒸馏引入跨层特征对齐机制。它将帧序列与数据包长度序列特征对齐，从而在增强语义与可观测序列之间实现跨层特征对齐。大量实验表明，SATA成功生成了训练集中缺失但测试集中真实存在的流量模式，并显著提升了主流模型在多样且复杂场景下的性能。特别是在开放世界设置中，SATA将ACC提升了90.81%，AUROC提升了48.37%。原型系统的源代码可在https://anonymous.4open.science/r/SATA-B6C2/获取。