🤖 AI Summary
研究解决了PQC演进对加密流量分类器的影响问题，通过构建基准测试并评估多种分类器在不同设置下的表现，揭示了跨域鲁棒性的重要性。
📝 Abstract
Encrypted traffic classifiers often achieve high accuracy under matched training and testing conditions, implicitly assuming that deployment traffic follows the training distribution. TLS migration toward post-quantum cryptography (PQC) challenges this assumption because hybrid key establishment can reshape observable traffic without changing application labels. We frame this change as PQC-induced protocol drift and study its effects through closed-world HTTPS website fingerprinting using the deployed TLS~1.3 Hybrid-PQC group \texttt{\detokenize{X25519MLKEM768}}. We build a controlled, PQC-aware benchmark pairing Traditional (Non-PQC) and Hybrid-PQC traffic, then evaluate five representative classifiers and side-channel representations under matched-domain, cross-domain, and deployment-ratio settings. Collectively, the experiments show that PQC evolution does not remove learnable website information. Instead, it changes how that information appears in traffic, causing classifiers and feature combinations that perform well in-domain to lose reliability across cryptographic domains. By exposing the fragility of matched-domain evaluation, we offer strategic guidance, identify cross-domain robustness as a research priority, and recommend protocol-aware practices for dependable real-world encrypted traffic classification. The code is available at http://anonymous.4open.science/r/PQ-WF-Eval.