LAUSR

Third-party tracking on the Web has been used for collecting and correlating user’s browsing behavior. Due to the increasing use of ad-blocking and third-party tracking protections, tracking providers introduced a new technique called CNAME cloaking. It misleads Web browsers into believing that a request for a subdomain of the visited website originates from this particular website, while this subdomain uses a CNAME to resolve to a tracking-related third-party domain. This technique thus circumvents the third-party targeting privacy protections. The goals of this paper are to characterize, detect, and protect the end-user against CNAME cloaking based tracking. Firstly, we characterize CNAME cloaking-based tracking by crawling top pages of the Alexa Top 300,000 sites and analyzing the usage of CNAME cloaking with CNAME blocklist, including websites and tracking providers using this technique to track users’ activities. We also point out that browsers and privacy protection extensions are largely ineffective to deal with CNAME cloaking-based tracking except for Firefox with a developer’s version of the uBlock Origin extension. Secondly, we propose a supervised machine learning-based approach to detect CNAME cloaking-based tracking without the on-demand DNS lookup. We show that the proposed approach outperforms well-known tracking filter lists. Finally, to circumvent the lack of DNS API in Chrome-based browsers, we design and implement a prototype of the supervised machine learning-based browser extension to detect and filter out CNAME cloaking tracking, called CNAMETracking Uncloaker. Our evaluation shows that CNAMETracking Uncloaker is able to filter out CNAME cloaking-based tracking requests without performance degradation when compared with the vanilla setting on the Chrome browser.