WebGraph: Capturing Advertising and Tracking Information Flows for Robust Blocking
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by
Sandra Siby, Umar Iqbal, Steven Englehardt, Zubair Shafiq, Carmela Troncoso
2021
Abstract
Millions of web users directly depend on ad and tracker blocking tools to
protect their privacy. However, existing ad and tracker blockers fall short
because of their reliance on trivially susceptible advertising and tracking
content. In this paper, we first demonstrate that the state-of-the-art machine
learning based ad and tracker blockers, such as AdGraph, are susceptible to
adversarial evasions deployed in real-world. Second, we introduce WebGraph, the
first graph-based machine learning blocker that detects ads and trackers based
on their action rather than their content. By building features around the
actions that are fundamental to advertising and tracking - storing an
identifier in the browser, or sharing an identifier with another tracker -
WebGraph performs nearly as well as prior approaches, but is significantly more
robust to adversarial evasions. In particular, we show that WebGraph achieves
comparable accuracy to AdGraph, while significantly decreasing the success rate
of an adversary from near-perfect under AdGraph to around 8% under WebGraph.
Finally, we show that WebGraph remains robust to a more sophisticated adversary
that uses evasion techniques beyond those currently deployed on the web.
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