针对多标签网页指纹攻击，提出新型Transformer模型，提升识别准确率。
Towards Robust Multi-tab Website Fingerprinting
- 将多标签浏览场景建模为多标签分类问题，用Transformer捕捉流量关联
- 在真实场景下实现最优识别效果，对防御手段仍具鲁棒性
- 适合研究隐私泄露与网络监控的学者，尤其关注Tor安全的研究者
网站指纹识别使监听者能够判断用户在加密连接中访问了哪些网站。现有最先进的网站指纹（WF）攻击在对抗Tor保护流量时仍具有效性，但其在多标签浏览会话中存在严重局限：个体网站的全局模式不再保留，且客户端打开的标签数量事先未知。本文提出ARES，一种专为多标签网站指纹攻击设计的新框架。ARES将多标签攻击建模为多标签分类问题，并利用新型基于Transformer的模型求解。具体而言，ARES通过多层次流量聚合特征提取局部模式，并采用改进的自注意力机制分析这些局部模式间的相关性，从而有效识别网站。我们实现了ARES原型，并使用持续数月收集的大规模数据集进行广泛评估。实验结果表明，ARES在多种现实场景中表现最优，且对各类网站指纹防御手段保持鲁棒性。
原文摘要 · Abstract (English)
Website fingerprinting enables an eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting (WF) attacks have demonstrated effectiveness even against Tor-protected network traffic. However, existing WF attacks have critical limitations on accurately identifying websites in multi-tab browsing sessions, where the holistic pattern of individual websites is no longer preserved, and the number of tabs opened by a client is unknown a priori. In this paper, we propose ARES, a novel WF framework natively designed for multi-tab WF attacks. ARES formulates the multi-tab attack as a multi-label classification problem and solves it using the novel Transformer-based models. Specifically, ARES extracts local patterns based on multi-level traffic aggregation features and utilizes the improved self-attention mechanism to analyze the correlations between these local patterns, effectively identifying websites. We implement a prototype of ARES and extensively evaluate its effectiveness using our large-scale datasets collected over multiple months. The experimental results illustrate that ARES achieves optimal performance in several realistic scenarios. Further, ARES remains robust even against various WF defenses.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。