In recent years, the website fingerprinting (WF) attack against the Tor anonymity system has become a hot research issue. The state-of-the-art WF studies have shown that the detection accuracy of websites is up to more than 95%. However, they are mainly conducted under the single-tab assumption, where each sample contains only one website traffic. The single-tab setting could not be realistic because users often open multiple tabs to browse simultaneously. The requests and responses from multiple tabs will overlap and interfere with each other, destroying existing single-tab WF attacks. In addition, the proposed multi-tab WF attack works poorly when traffic overlaps seriously. It remains challenging to implement WF attacks in multi-tab scenarios. This paper investigates a new spatial separated boxes-based multi-tab website fingerprinting model, called SSBM, to solve the multi-tab WF problem. It is an end-to-end model that separates traffic by equal-sized boxes and extracts features with convolutional neural networks. By predicting the label of each box, the tabs of the whole traffic are inferred. We design and implement SSBM and compare it with state-of-the-art multi-tab WF attacks in two different multi-tab modes: overlapping mode and delayed mode. In the overlapping mode, SSBM can successfully identify 81.24% of the first tab and 64.72% of the second tab when the overlapping proportions of the two tabs’ traffic reaches 50%, which are 4% and 29% higher than the current strongest BAPM. In the delayed mode, when the second tab traffic starts to overlap with the first tab traffic with a 5-second delay, SSBM improves the first tab’s classification accuracy from 60% to 69% and the second tab’s detection rates from 33% to 53%. Moreover, SSBM achieves the highest improvement, nearly 40%, in the three-tab evaluations. The experimental results show that SSBM outperforms existing multi-tab WF attack methods.

中文翻译：

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SSBM：一种基于空间分离框的多标签页网站指纹识别模型


近年来，针对 Tor 匿名系统的网站指纹识别 （WF） 攻击已成为一个热门的研究问题。最先进的 WF 研究表明，网站的检测准确率高达 95% 以上。但是，它们主要在单标签假设下进行，其中每个样本仅包含一个网站流量。单选项卡设置不现实，因为用户经常打开多个选项卡以同时浏览。来自多个选项卡的请求和响应将重叠并相互干扰，从而销毁现有的单选项卡 WF 攻击。此外，当流量严重重叠时，建议的多选项卡 WF 攻击效果不佳。在多选项卡方案中实施 WF 攻击仍然具有挑战性。本文研究了一种新的基于空间分隔框的多标签页网站指纹识别模型，称为 SSBM，以解决多标签页 WF 问题。它是一个端到端模型，通过大小相等的框分隔流量，并使用卷积神经网络提取特征。通过预测每个框的标签，可以推断出整个流量的选项卡。我们设计并实施了 SSBM，并将其与两种不同多选项卡模式（重叠模式和延迟模式）中最先进的多选项卡 WF 攻击进行了比较。在重叠模式下，当两个标签页流量的重叠比例达到 50% 时，SSBM 可以成功识别出 81.24% 的第一页签和 64.72% 的第二页签，分别比当前最强的 BAPM 高出 4% 和 29%。在延迟模式下，当第二个标签页流量开始与第一个标签页流量重叠时，延迟 5 秒时，SSBM 将第一个标签页的分类准确率从 60% 提升到 69%，第二个标签页的检测率从 33% 提升到 53%。 此外，SSBM 在三标签评估中实现了最高的改进，接近 40%。实验结果表明，SSBM 优于现有的多标签 WF 攻击方法。