Website fingerprinting (WF) attacks identify the websites visited over anonymized connections by analyzing patterns in network traffic flows, such as packet sizes, directions, or interval times using a machine learning classifier. Previous studies showed WF attacks achieve high classification accuracy. However, several issues call into question whether existing WF approaches are realizable in practice and thus motivate a re-exploration. Due to Tor's performance issues and resulting poor browsing experience, the vast majority of users opt for Virtual Private Networking (VPN) despite VPNs weaker privacy protections. Many other past assumptions are increasingly unrealistic as web technology advances. Our work addresses several key limitations of prior art. First, we introduce a new approach that classifies entire websites rather than individual web pages. Site-level classification uses traffic from all site components, including advertisements, multimedia, and single-page applications. Second, our Convolutional Neural Network (CNN) uses only the jitter and size of 500 contiguous packets from any point in a TCP stream, in contrast to prior work requiring heuristics to find page boundaries. Our seamless approach makes eavesdropper attack models realistic. Using traces from a controlled browser, we show our CNN matches observed traffic to a website with over 90% accuracy. We found the training traffic quality is critical as classification accuracy is significantly reduced when the training data lacks variability in network location, performance, and clients' computational capability. We enhanced the base CNN's efficacy using domain adaptation, allowing it to discount irrelevant features, such as network location. Lastly, we evaluate several defensive strategies against seamless WF attacks.

翻译：网站指纹识别（WF）攻击通过分析网络流量模式（如数据包大小、方向或间隔时间），利用机器学习分类器识别匿名化连接中访问的网站。先前研究表明，WF攻击能够实现较高的分类准确率。然而，若干问题引发了对现有WF方法实际可行性的质疑，从而促使我们重新审视该领域。由于Tor网络的性能问题导致浏览体验不佳，绝大多数用户选择使用虚拟专用网络（VPN），尽管VPN的隐私保护能力较弱。随着网络技术的发展，许多过去的假设日益脱离现实。本研究针对现有技术的若干关键局限进行了改进。首先，我们提出了一种对完整网站而非单个网页进行分类的新方法。站点级分类利用网站所有组件的流量，包括广告、多媒体和单页应用程序。其次，我们的卷积神经网络（CNN）仅使用TCP流中任意连续500个数据包的抖动和大小特征，而以往研究需要启发式方法确定页面边界。这种无缝方法使窃听者攻击模型更具现实可行性。通过受控浏览器流量轨迹实验，我们证明该CNN能以超过90%的准确率将观测流量与特定网站匹配。研究发现训练流量质量至关重要：当训练数据缺乏网络位置、性能及客户端计算能力的多样性时，分类准确率会显著下降。我们通过域自适应技术增强了基础CNN的效能，使其能够忽略不相关特征（如网络位置）。最后，我们评估了多种针对无缝WF攻击的防御策略。