Detecting Anti Ad-blockers in the Wild
Detecting Anti Ad-blockers in the Wild
复制标题
DOI：
10.1515/popets-2017-0032
复制
发表时间：
2017-07
影响因子：
--
通讯作者：
M. Mughees;Zhiyun Qian;Zubair Shafiq
中科院分区：
文献类型：
--
作者：
M. Mughees;Zhiyun Qian;Zubair Shafiq
广告拦截器的兴起被在线出版商视为一种经济威胁，他们主要依靠在线广告来赚钱。为了应对这一威胁，出版商已经开始通过使用反广告拦截器进行报复，这些反广告拦截器会搜索广告拦截用户，并通过推动用户将网站列入白名单或完全禁用广告拦截器来对他们做出反应。广告拦截器和反广告拦截器之间的冲突导致了网络上新的军备竞赛。在本文中，我们提出了一种基于自动机器学习的方法来识别反广告拦截器，以检测和响应广告拦截用户。该方法的准确率为94.8%，召回率为93.1%。我们的自动化方法使我们能够在Alexa前100K网站上进行大规模的反广告拦截剂测量研究。我们确定了686个网站，使其页面内容的可见变化，以响应广告拦截检测。我们描述了抗广告阻断剂使用的不同策略的频谱。我们发现，大多数发布者使用相当简单的第一方反广告拦截脚本。然而，我们也注意到第三方反广告拦截服务的使用，这些服务使用更复杂的策略来检测和响应广告拦截器。
Abstract The rise of ad-blockers is viewed as an economic threat by online publishers who primarily rely on online advertising to monetize their services. To address this threat, publishers have started to retaliate by employing anti ad-blockers, which scout for ad-block users and react to them by pushing users to whitelist the website or disable ad-blockers altogether. The clash between ad-blockers and anti ad-blockers has resulted in a new arms race on the Web. In this paper, we present an automated machine learning based approach to identify anti ad-blockers that detect and react to ad-block users. The approach is promising with precision of 94.8% and recall of 93.1%. Our automated approach allows us to conduct a large-scale measurement study of anti ad-blockers on Alexa top-100K websites. We identify 686 websites that make visible changes to their page content in response to ad-block detection. We characterize the spectrum of different strategies used by anti ad-blockers. We find that a majority of publishers use fairly simple first-party anti ad-block scripts. However, we also note the use of third-party anti ad-block services that use more sophisticated tactics to detect and respond to ad-blockers.