The ad wars: retrospective measurement and analysis of anti-adblock filter lists
The ad wars: retrospective measurement and analysis of anti-adblock filter lists
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DOI：
10.1145/3131365.3131387
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Umar Iqbal;Zubair Shafiq;Zhiyun Qian
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文献类型：
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作者：
Umar Iqbal;Zubair Shafiq;Zhiyun Qian
广告拦截器的日益流行促使在线发布商通过部署反广告拦截脚本来报复广告拦截用户，该脚本检测广告拦截用户并阻止他们访问内容，除非他们禁用广告拦截器。为了规避反广告拦截器，广告拦截器依赖于手动策划的反广告拦截过滤器列表来删除反广告拦截脚本。反广告拦截过滤列表目前依赖于来自用户的非正式众包反馈来添加/删除过滤列表规则。在本文中，我们首次全面研究了反广告拦截过滤器列表，以分析其对反广告拦截器的有效性。具体来说，我们比较和对比两个流行的反广告过滤器列表的演变。我们表明，这些过滤器列表的实现非常不同，即使他们目前有相当数量的过滤器列表规则。然后，我们使用互联网档案馆的Wayback Machine对过去五年中Alexa前5 K网站上的这些过滤器列表进行回顾性覆盖分析。我们发现，自2014年以来，这些过滤器列表的覆盖率有了很大的提高，它们在大约9%的Alexa前5 K网站上检测到了反广告拦截器。为了提高过滤器列表的覆盖率和加速添加新的过滤器规则，我们还设计和实现了一种基于机器学习的方法，使用静态JavaScript代码分析自动检测反广告拦截脚本。
The increasing popularity of adblockers has prompted online publishers to retaliate against adblock users by deploying anti-adblock scripts, which detect adblock users and bar them from accessing content unless they disable their adblocker. To circumvent anti-adblockers, adblockers rely on manually curated anti-adblock filter lists for removing anti-adblock scripts. Anti-adblock filter lists currently rely on informal crowdsourced feedback from users to add/remove filter list rules. In this paper, we present the first comprehensive study of anti-adblock filter lists to analyze their effectiveness against anti-adblockers. Specifically, we compare and contrast the evolution of two popular anti-adblock filter lists. We show that these filter lists are implemented very differently even though they currently have a comparable number of filter list rules. We then use the Internet Archive's Wayback Machine to conduct a retrospective coverage analysis of these filter lists on Alexa top-5K websites over the span of last five years. We find that the coverage of these filter lists has considerably improved since 2014 and they detect anti-adblockers on about 9% of Alexa top-5K websites. To improve filter list coverage and speedup addition of new filter rules, we also design and implement a machine learning based method to automatically detect anti-adblock scripts using static JavaScript code analysis.