TrackerDetector: A system to detect third-party trackers through machine learning

TrackerDetector: A system to detect third-party trackers through machine learning
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TrackerDetector:通过机器学习检测第三方跟踪器的系统

DOI:
10.1016/j.comnet.2015.08.012
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发表时间:
2015-11
期刊:
影响因子:
5.6
通讯作者:
Guanxing Wen
Guanxing Wen
中科院分区:
计算机科学3区
文献类型:
--
作者:
QianruWu;Qixu Liu;Yuqing Zhang;Guanxing Wen

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由第三方跟踪引起的隐私侵犯已经成为一个严重的问题,最有效的防御方法是阻止。然而,作为拦截的核心部分,黑名单通常是人工策划的,并且很难维护。为了更容易生成黑名单,减少人工工作,我们提出了一个有效的系统,具有高精度,名为TrackerDetector,自动检测第三方跟踪器。直觉上,跟踪器和非跟踪器的行为是不同的,这导致调用不同的JavaScript API集。因此,增量分类器是从从大量网站抓取的JavaScript文件训练的,以检测网站是否是第三方跟踪器。使用我们的数据集获得了97.34%的高准确度,并且在10倍交叉验证中获得了93.56%的准确度。
Privacy violation caused by third-party tracking has become a serious problem, and the most effective defense against it is blocking. However, as the core part of blocking, the blacklist is usually manually curated and is difficult to maintain. To make it easier to generate a blacklist and reduce human work, we propose an effective system with high accuracy, named TrackerDetector, to detect third-party trackers automatically. Intuitively, the behaviors of trackers and non-trackers are different, which leads to different JavaScript API sets being called. Thus, an incremental classifier is trained from JavaScript files crawled from a large number of websites to detect whether a website is a third-party tracker. High accuracy of 97.34% is obtained with our dataset and that of 93.56% is obtained within a 10-fold cross validation.
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