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Websites use third-party ads and tracking services to deliver targeted adsand collect information about users that visit them. These services put users'privacy at risk, and that is why users' demand for blocking these services isgrowing. Most of the blocking solutions rely on crowd-sourced filter listsmanually maintained by a large community of users. In this work, we seek tosimplify the update of these filter lists by combining different websitesthrough a large-scale graph connecting all resource requests made over a largeset of sites. The features of this graph are extracted and used to train amachine learning algorithm with the aim of detecting ads and trackingresources. As our approach combines different information sources, it is morerobust toward evasion techniques that use obfuscation or changing the usagepatterns. We evaluate our work over the Alexa top-10K websites and find itsaccuracy to be 96.1% biased and 90.9% unbiased with high precision and recall.It can also block new ads and tracking services, which would necessitate beingblocked by further crowd-sourced existing filter lists. Moreover, the approachfollowed in this paper sheds light on the ecosystem of third-party tracking andadvertising.

Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain Graph