论文标题
来自TLS痕迹的自适应网页指纹
Adaptive Webpage Fingerprinting from TLS Traces
论文作者
论文摘要
在网页指纹识别中，一门对手通过分析用户浏览器和网站服务器之间交换的加密TLS流量中的模式，从而渗透了受害者用户加载的特定网页。这项工作研究了针对TLS协议的现代网页指纹对手；旨在阐明其能力并为潜在的防御能力提供信息。尽管该研究领域的重要性（大多数全球互联网用户都依赖于使用TLS的标准Web浏览）和潜在的现实生活影响，但过去的大多数工作都集中在特定于匿名网络（例如TOR）的攻击上。我们介绍了一个特定于TLS的模型：1）缩放到前所未有的目标网页，2）可以准确地对培训期间从未遇到过的数千个类别进行分类，而3）即使在频繁的页面更新的情况下，也具有较低的操作成本。基于这些发现，我们然后讨论TLS特定的对策，并评估TLS 1.3提供的现有填充功能的有效性。
In webpage fingerprinting, an on-path adversary infers the specific webpage loaded by a victim user by analysing the patterns in the encrypted TLS traffic exchanged between the user's browser and the website's servers. This work studies modern webpage fingerprinting adversaries against the TLS protocol; aiming to shed light on their capabilities and inform potential defences. Despite the importance of this research area (the majority of global Internet users rely on standard web browsing with TLS) and the potential real-life impact, most past works have focused on attacks specific to anonymity networks (e.g., Tor). We introduce a TLS-specific model that: 1) scales to an unprecedented number of target webpages, 2) can accurately classify thousands of classes it never encountered during training, and 3) has low operational costs even in scenarios of frequent page updates. Based on these findings, we then discuss TLS-specific countermeasures and evaluate the effectiveness of the existing padding capabilities provided by TLS 1.3.
