WebGraph: Capturing Advertising and Tracking Information Flows for Robust Blocking
WebGraph: Capturing Advertising and Tracking Information Flows for Robust Blocking
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发表时间：
2021-07
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通讯作者：
S. Siby;Umar Iqbal;Steven Englehardt;Zubair Shafiq;C. Troncoso
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作者：
S. Siby;Umar Iqbal;Steven Englehardt;Zubair Shafiq;C. Troncoso
数以百万计的网络用户直接依赖广告和跟踪器阻止工具来保护他们的隐私。然而，现有的广告和跟踪拦截器由于依赖于容易受到影响的广告和跟踪内容而不足。在本文中，我们首先证明了最先进的基于机器学习的广告和跟踪器拦截器，如AdGraph，容易受到现实世界中部署的对抗性规避的影响。其次，我们介绍了WebGraph，这是第一个基于图形的机器学习拦截器，它可以根据广告和跟踪器的动作而不是内容来检测它们。通过围绕广告和跟踪的基本动作构建功能-在浏览器中存储标识符，或与另一个跟踪器共享标识符- WebGraph的性能几乎与之前的方法一样好，但对对抗性规避的鲁棒性更强。特别是，我们表明，WebGraph实现了与AdGraph相当的准确性，同时显着降低了对手的成功率，从AdGraph下的近乎完美到WebGraph下的8%左右。最后，我们表明，WebGraph仍然强大的一个更复杂的对手，使用规避技术超出目前部署在网络上。
Millions of web users directly depend on ad and tracker blocking tools to protect their privacy. However, existing ad and tracker blockers fall short because of their reliance on trivially susceptible advertising and tracking content. In this paper, we first demonstrate that the state-of-the-art machine learning based ad and tracker blockers, such as AdGraph, are susceptible to adversarial evasions deployed in real-world. Second, we introduce WebGraph, the first graph-based machine learning blocker that detects ads and trackers based on their action rather than their content. By building features around the actions that are fundamental to advertising and tracking - storing an identifier in the browser, or sharing an identifier with another tracker - WebGraph performs nearly as well as prior approaches, but is significantly more robust to adversarial evasions. In particular, we show that WebGraph achieves comparable accuracy to AdGraph, while significantly decreasing the success rate of an adversary from near-perfect under AdGraph to around 8% under WebGraph. Finally, we show that WebGraph remains robust to a more sophisticated adversary that uses evasion techniques beyond those currently deployed on the web.