WebGraph: Capturing Advertising and Tracking Information Flows for Robust Blocking release_7xte34l65zdybo3vglws74m6pa

by Sandra Siby, Umar Iqbal, Steven Englehardt, Zubair Shafiq, Carmela Troncoso

Released as a article .

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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Type  article
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Date   2021-08-17
Version   v2
Language   en ?
arXiv  2107.11309v2
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