Wide-AdGraph：利用宽依赖链图检测广告跟踪器
Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain Graph
摘要：
网站使用第三方广告和跟踪服务来提供有针对性的广告并收集有关访问它们的用户的信息。这些服务将用户的隐私置于危险之中，这就是为什么用户对阻止这些服务的需求越来越大。大多数屏蔽解决方案都依赖于由大型用户社区手动维护的众包过滤器列表。在这项工作中，我们试图简化这些过滤器列表的更新，通过一个大规模的图形连接在一个大的网站集的所有资源请求，结合不同的网站。该图的特征被提取并用于训练机器学习算法，其目的是检测广告和跟踪资源。由于我们的方法结合了不同的信息源，它对使用混淆或改变使用模式的规避技术更鲁棒。我们在Alexa前10K网站上评估了我们的工作，发现其准确率为96.1%有偏差，90.9%无偏差，具有高精度和召回率。它还可以阻止新的广告和跟踪服务，这将需要通过进一步的众包现有过滤器列表来阻止。此外，本文采用的方法揭示了第三方跟踪和广告的生态系统。
英文摘要：
Websites use third-party ads and tracking services to deliver targeted ads and collect information about users that visit them. These services put users' privacy at risk, and that is why users' demand for blocking these services is growing. Most of the blocking solutions rely on crowd-sourced filter lists manually maintained by a large community of users. In this work, we seek to simplify the update of these filter lists by combining different websites through a large-scale graph connecting all resource requests made over a large set of sites. The features of this graph are extracted and used to train a machine learning algorithm with the aim of detecting ads and tracking resources. As our approach combines different information sources, it is more robust toward evasion techniques that use obfuscation or changing the usage patterns. We evaluate our work over the Alexa top-10K websites and find its accuracy to be 96.1% biased and 90.9% unbiased with high precision and recall. It can also block new ads and tracking services, which would necessitate being blocked by further crowd-sourced existing filter lists. Moreover, the approach followed in this paper sheds light on the ecosystem of third-party tracking and advertising.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
