AdGraph: A Graph-Based Approach to Ad and Tracker Blocking
AdGraph: A Graph-Based Approach to Ad and Tracker Blocking
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DOI：
10.1109/sp40000.2020.00005
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发表时间：
2018-05
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通讯作者：
Umar Iqbal;Peter Snyder;Shitong Zhu;B. Livshits;Zhiyun Qian;Zubair Shafiq
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作者：
Umar Iqbal;Peter Snyder;Shitong Zhu;B. Livshits;Zhiyun Qian;Zubair Shafiq
用户对屏蔽广告和在线跟踪的需求很大，而且还在不断增长。现有的工具，无论是已部署的还是在研究中描述的，都被证明是有用的，但缺乏通用解决方案所需的完整性或健壮性。现有的检测方法通常只关注广告或跟踪的一个方面（例如URL模式、代码结构），这使得现有的方法很容易被逃避。在这项工作中，我们提出了一种新的基于图的机器学习方法AdGraph，用于检测广告和跟踪网络资源。AdGraph与现有方法的不同之处在于，它构建了网页的HTML结构、网络请求和JavaScript行为的图形表示，并使用这种独特的表示来训练分类器，以识别广告和跟踪资源。由于AdGraph考虑了网络请求发生的环境的许多方面，因此它不太容易受到单一因素逃避技术的影响，而这些技术使现有方法陷入混乱。我们在Alexa top-10K网站上对AdGraph进行了评估，发现它具有很高的准确性，能够以95.33%的准确率复制人工生成的过滤列表的标签，甚至可以识别出过滤列表中的许多错误。我们实现AdGraph是对Chromium的修改。AdGraph在页面加载和执行上只增加了很小的开销，实际上在42%的网站上比Chromium快，在78%的网站上比AdBlock Plus快。总的来说，我们得出的结论是，对于在线使用来说，AdGraph既足够准确，性能也足够好，与流行的基于过滤列表的方法相比，它可以破坏可比或更少的网站。
User demand for blocking advertising and tracking online is large and growing. Existing tools, both deployed and described in research, have proven useful, but lack either the completeness or robustness needed for a general solution. Existing detection approaches generally focus on only one aspect of advertising or tracking (e.g. URL patterns, code structure), making existing approaches susceptible to evasion.In this work we present AdGraph, a novel graph-based machine learning approach for detecting advertising and tracking resources on the web. AdGraph differs from existing approaches by building a graph representation of the HTML structure, network requests, and JavaScript behavior of a webpage, and using this unique representation to train a classifier for identifying advertising and tracking resources. Because AdGraph considers many aspects of the context a network request takes place in, it is less susceptible to the single-factor evasion techniques that flummox existing approaches.We evaluate AdGraph on the Alexa top-10K websites, and find that it is highly accurate, able to replicate the labels of human-generated filter lists with 95.33% accuracy, and can even identify many mistakes in filter lists. We implement AdGraph as a modification to Chromium. AdGraph adds only minor overhead to page loading and execution, and is actually faster than stock Chromium on 42% of websites and AdBlock Plus on 78% of websites. Overall, we conclude that AdGraph is both accurate enough and performant enough for online use, breaking comparable or fewer websites than popular filter list based approaches.