Key points are not available for this paper at this time.
To better understand the demographics of their visitors and their paths through their websites, the vast majority of modern website owners make use of third-party analytics platforms, such as, Google Analytics and ClickTale. Given that all the clients of a third-party analytics platform report to the same server, the tracking requests need to contain identifiers that allow the analytics server to differentiate between their clients. In this paper, we analyze the analytics identifiers utilized by eighteen different third-party analytics platforms and show that these identifiers enable the clustering of seemingly unrelated websites as part of a common third-party analytics account (i.e. websites whose analytics are managed by a single person or team). We focus our attention on malicious websites that also utilize third-party web analytics and show that threat analysts can utilize web analytics to both discover previously unknown malicious pages in a threat-agnostic fashion, as well as to cluster malicious websites into campaigns. We build a system for automatically identifying, isolating, and querying analytics identifiers from malicious pages and use it to discover an additional 11K live domains that use analytics associated with malicious pages. We show how our system can be used to improve the coverage of existing blacklists, discover previously unknown phishing campaigns, identify malicious binaries and Android apps, and even aid in attribution of malicious domains with protected WHOIS information.
Starov et al. (Mon,) studied this question.