An approach for identifying JavaScript-loaded advertisements through static program analysis
An approach for identifying JavaScript-loaded advertisements through static program analysis
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DOI：
10.1145/2381966.2381968
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
Caitlin R. Orr;A. Chauhan;Minaxi Gupta;Chris Frisz;Christopher W. Dunn
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文献类型：
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作者：
Caitlin R. Orr;A. Chauhan;Minaxi Gupta;Chris Frisz;Christopher W. Dunn
出于与隐私、突兀性和安全性相关的原因，人们对屏蔽广告的前景非常感兴趣。目前实现这一目标的方法包括保持基于URL的正则表达式集，这些正则表达式与从网页上获取的每个URL进行匹配。虽然这种方法通常是有效的，但它是不可伸缩的，并且需要经常手工维护过滤列表。为了克服这些缺点，我们提出了一种完全不同的方法，我们展示了JavaScript源代码的静态程序分析可以用来识别加载和显示广告的JavaScript。我们使用静态分析可以让我们在运行前标记和阻止广告相关的脚本，除了阻止广告之外，还提供了安全性。我们开发的特征训练的分类器的初步结果在识别广告相关脚本方面达到98%的准确率。
Motivated by reasons related to privacy, obtrusiveness, and security, there is great interest in the prospect of blocking advertisements. Current approaches to this goal involve keeping sets of URL-based regular expressions, which are matched against every URL fetched on a web page. While generally effective, this approach is not scalable and requires constant manual maintenance of the filtering lists. To counter these shortcomings, we present a fundamentally different approach with which we demonstrate that static program analysis on JavaScript source code can be used to identify JavaScript that loads and displays ads. Our use of static analysis lets us flag and block ad-related scripts before runtime, offering security in addition to blocking ads. Preliminary results from a classifier trained on the features we develop achieve 98% accuracy in identifying ad-related scripts.