Encrypted web traffic can reveal sensitive information of users, such as their browsing behaviors. Existing studies on encrypted traffic analysis focus on website fingerprinting. We claim that fine-grained webpage fingerprinting, which speculates specific webpages on a same website visited by a victim, allows exploiting more user private information, e.g., shopping interests in an online shopping mall. Since webpages from the same website usually have very similar traffic traces that make them indistinguishable, existing solutions may end up with low accuracy. In this paper, we propose FineWP, a novel fine-grained webpage fingerprinting method. We make an observation that the length information of packets in bidirectional client-server interactions can be distinctive features for webpage fingerprinting. The extracted features are then fed into traditional machine learning models to train classifiers, which achieve both high accuracy and low training overhead. We collect two real-world traffic datasets and construct closed- and open-world evaluations to verify the effectiveness of FineWP. The experimental results demonstrate that FineWP is superior to the state-of-the-art methods in terms of accuracy, time complexity and stability.

中文翻译：

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仅使用加密流量的数据包长度信息的细粒度网页指纹识别


加密的网络流量可能会泄露用户的敏感信息，例如他们的浏览行为。现有的加密流量分析研究主要集中在网站指纹识别上。我们声称，细粒度的网页指纹识别可以推测受害者访问的同一网站上的特定网页，从而可以利用更多的用户私人信息，例如在线购物中心的购物兴趣。由于来自同一网站的网页通常具有非常相似的流量轨迹，使得它们难以区分，因此现有的解决方案可能会导致准确性较低。在本文中，我们提出了 FineWP，一种新颖的细粒度网页指纹识别方法。我们观察到，双向客户端-服务器交互中数据包的长度信息可以成为网页指纹识别的显着特征。然后将提取的特征输入传统的机器学习模型来训练分类器，从而实现高精度和低训练开销。我们收集了两个真实世界的流量数据集，并构建封闭和开放世界的评估来验证 FineWP 的有效性。实验结果表明，FineWP 在准确性、时间复杂度和稳定性方面优于最先进的方法。