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Millions of web users directly depend on ad and tracker blocking tools toprotect their privacy. However, existing ad and tracker blockers fall shortbecause of their reliance on trivially susceptible advertising and trackingcontent. In this paper, we first demonstrate that the state-of-the-art machinelearning based ad and tracker blockers, such as AdGraph, are susceptible toadversarial evasions deployed in real-world. Second, we introduce WebGraph, thefirst graph-based machine learning blocker that detects ads and trackers basedon their action rather than their content. By building features around theactions that are fundamental to advertising and tracking - storing anidentifier in the browser, or sharing an identifier with another tracker -WebGraph performs nearly as well as prior approaches, but is significantly morerobust to adversarial evasions. In particular, we show that WebGraph achievescomparable accuracy to AdGraph, while significantly decreasing the success rateof an adversary from near-perfect under AdGraph to around 8% under WebGraph.Finally, we show that WebGraph remains robust to a more sophisticated adversarythat uses evasion techniques beyond those currently deployed on the web.

WebGraph: Capturing Advertising and Tracking Information Flows for Robust Blocking