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User demand for blocking advertising and tracking online is large andgrowing. Existing tools, both deployed and described in research, have provenuseful, but lack either the completeness or robustness needed for a generalsolution. Existing detection approaches generally focus on only one aspect ofadvertising or tracking (e.g. URL patterns, code structure), making existingapproaches susceptible to evasion.  In this work we present AdGraph, a novel graph-based machine learningapproach for detecting advertising and tracking resources on the web. AdGraphdiffers from existing approaches by building a graph representation of the HTMLstructure, network requests, and JavaScript behavior of a webpage, and usingthis unique representation to train a classifier for identifying advertisingand tracking resources. Because AdGraph considers many aspects of the context anetwork request takes place in, it is less susceptible to the single-factorevasion techniques that flummox existing approaches.  We evaluate AdGraph on the Alexa top-10K websites, and find that it is highlyaccurate, able to replicate the labels of human-generated filter lists with95.33% accuracy, and can even identify many mistakes in filter lists. Weimplement AdGraph as a modification to Chromium. AdGraph adds only minoroverhead to page loading and execution, and is actually faster than stockChromium on 42% of websites and AdBlock Plus on 78% of websites. Overall, weconclude that AdGraph is both accurate enough and performant enough for onlineuse, breaking comparable or fewer websites than popular filter list basedapproaches.

AdGraph: A Graph-Based Approach to Ad and Tracker Blocking