Errors, Misunderstandings, and Attacks: Analyzing the Crowdsourcing Process of Ad-blocking Systems
Errors, Misunderstandings, and Attacks: Analyzing the Crowdsourcing Process of Ad-blocking Systems
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错误、误解和攻击：分析广告拦截系统的众包流程
DOI：
10.1145/3355369.3355588
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Wang, Gang
中科院分区：
文献类型：
--
作者：
Alrizah, Mshabab;Zhu, Sencun;Xing, Xinyu;Wang, Gang
Adblock Plus等广告拦截系统依靠众包来构建和维护过滤器列表，这是确定在网页上拦截哪些广告的基础。在这项工作中，我们试图提高我们对广告拦截社区以及众包过程中的错误和陷阱的理解。为此，我们收集和分析了一个纵向数据集，涵盖了流行的过滤器列表EasyList的动态变化，在同一时期的人群提交的错误报告，我们的研究产生了一些重要的发现，关于FP和FN错误的特点和原因。例如，我们发现假阳性错误（即，错误地阻止合法内容）仍然需要很长时间才能被发现（其中50%需要一个多月），尽管社区做出了努力。EasyList的编辑和网站所有者都应该为误报负责。此外，我们发现大量的假阴性错误（即，未能阻止真实的广告）要么被错误地报道，要么被编辑简单地忽略。此外，我们还分析了广告发布商对广告拦截器的规避攻击。总的来说，我们的分析涵盖了15种类型的攻击方法，包括8种尚未被研究界研究的方法。我们展示了广告发布商如何利用它们来规避广告拦截器，并根据经验衡量广告拦截器的反应。通过深入的分析，我们的研究结果有望有助于阐明未来任何发展广告拦截和优化众包机制的工作。
Ad-blocking systems such as Adblock Plus rely on crowdsourcing to build and maintain filter lists, which are the basis for determining which ads to block on web pages. In this work, we seek to advance our understanding of the ad-blocking community as well as the errors and pitfalls of the crowdsourcing process. To do so, we collected and analyzed a longitudinal dataset that covered the dynamic changes of popular filter-list EasyList for nine years and the error reports submitted by the crowd in the same period.Our study yielded a number of significant findings regarding the characteristics of FP and FN errors and their causes. For instances, we found that false positive errors (i.e., incorrectly blocking legitimate content) still took a long time before they could be discovered (50% of them took more than a month) despite the community effort. Both EasyList editors and website owners were to blame for the false positives. In addition, we found that a great number of false negative errors (i.e., failing to block real advertisements) were either incorrectly reported or simply ignored by the editors. Furthermore, we analyzed evasion attacks from ad publishers against ad-blockers. In total, our analysis covers 15 types of attack methods including 8 methods that have not been studied by the research community. We show how ad publishers have utilized them to circumvent ad-blockers and empirically measure the reactions of ad blockers. Through in-depth analysis, our findings are expected to help shed light on any future work to evolve ad blocking and optimize crowdsourcing mechanisms.
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