While Website Fingerprinting (WF) attacks achieve high accuracy in controlled laboratory settings, they often degrade substantially in real-world environments due to spatio-temporal drift, browser heterogeneity, proxy obfuscation and etc. This limitation stems from their sole reliance on low-level traffic features that are noisy and highly sensitive to environmental perturbations. To address this problem, we propose \textbf{ResAware}, a cross-environment resource-aware distillation framework under a \textit{training-rich/inference-poor} asymmetric setting. Specifically, ResAware trains a teacher model on resource-level features, and then distills the resulting privileged knowledge into a student model through heterogeneous knowledge distillation. At deployment time, the student model performs inference using only encrypted traffic, incurring zero additional cost. We evaluate ResAware on a large-scale dataset collected over five months from six globally distributed vantage points, comprising more than $160{,}000$ paired samples. The results show that ResAware significantly enhances the cross-environment robustness of diverse WF baselines. Under a 150-day temporal drift, for example, ResAware improves the F1-score of Var-CNN from $72.77\%$ to $81.49\%$ and the open-world $TPR@1\%FPR$ from $22.40\%$ to $27.20\%$. Our results demonstrate that resource-level supervision improves WF robustness without expanding online observation capabilities.

翻译：尽管网站指纹识别（WF）攻击在受控实验室环境中取得了高精度，但在实际环境中，由于时空漂移、浏览器异构性、代理混淆等因素，其性能往往会大幅下降。这一局限性源于攻击仅依赖于低层次流量特征，而这些特征存在噪声且对环境扰动高度敏感。为解决该问题，我们提出了一种跨环境资源感知蒸馏框架 \textbf{ResAware}，该框架在“训练丰富/推断贫乏”的非对称设置下运行。具体而言，ResAware 在资源级特征上训练教师模型，然后通过异构知识蒸馏将获得的特权知识迁移至学生模型。在部署时，学生模型仅利用加密流量进行推理，不产生任何额外成本。我们在一个跨五个月收集的、来自六个全球分布式观测点的数据集上评估了ResAware，该数据集包含超过 16 万个配对样本。结果表明，ResAware 显著增强了多种WF基线方法的跨环境鲁棒性。例如，在150天的时空漂移条件下，ResAware 将Var-CNN的F1分数从 72.77% 提升至 81.49%，将开放世界中的 $TPR@1\%FPR$ 从 22.40% 提升至 27.20%。我们的实验证明，资源级监督能够在不扩展在线观测能力的前提下提升WF的鲁棒性。