发表机构
National University of Defense Technology（国防科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对加密流量分类中鲁棒优化成本高的问题，提出PERO框架，通过轻量代理估计风险并选择高风险样本更新模型，在保持性能的同时降低了计算与内存成本。
AI 中文摘要
加密流量分类对网络安全至关重要，但实际部署中对罕见但高损失的错误（如恶意流量分类错误）极为敏感。加密流量 foundation 模型作为一种有前景的通用技术，可实现出色的整体性能。然而，采用经验风险最小化等标准目标函数常忽略高风险尾部事件，常用性能指标也难以反映风险敏感场景下的鲁棒性局限。将条件风险价值等鲁棒优化目标直接应用于预训练后阶段，对大模型而言计算成本过高，因为识别高损失样本会消耗大量计算资源。为此，我们提出 Pre-Evaluation Robust Optimization（PERO，预评估鲁棒优化），这是一种面向加密流量 foundation 模型的高效鲁棒预训练后框架。PERO 采用轻量代理估计样本级风险，并选择高风险样本子集更新 foundation 模型，将风险估计与昂贵的大模型优化解耦。在典型加密流量数据集上的大量实验表明，PERO 相比现有优秀的鲁棒预训练后方法，具有相当或更优的鲁棒性和平均性能，同时大幅降低了计算和内存成本。
英文摘要
Encrypted traffic classification is vital for network security, yet real-world deployments are inherently sensitive to rare but high-loss errors such as misclassification of malicious traffic. The encrypted traffic foundation model, as a promising general-purpose technique, can achieve impressive overall performance. However, employing standard objectives such as empirical risk minimization often overlooks high-risk tail events, and commonly used performance metrics hardly reflect robustness limitations in risk-sensitive scenarios. Directly applying robust optimization objectives, such as conditional value-at-risk, to post-training is computationally prohibitive for large models, as identifying high-loss samples exhausts substantial computation. To this end, we propose Pre-Evaluation Robust Optimization (PERO), an efficient robust post-training framework for encrypted traffic foundation models. PERO employs a lightweight proxy to estimate sample-wise risk and selects a subset of high-risk samples to update the foundation model, decoupling risk estimation from expensive large-model optimization. Extensive experiments on typical encrypted traffic datasets show that PERO achieves competitive or superior robustness and average performance compared to outstanding robust post-training methods, while significantly reducing computational and memory costs.
Comments16 pages, 6 figures, 6 tables, conference
