AutoFR: Automated Filter Rule Generation for Adblocking


Hieu Le, Salma Elmalaki, and Athina Markopoulou, University of California, Irvine; Zubair Shafiq, University of California, Davis


Adblocking relies on filter lists, which are manually curated and maintained by a community of filter list authors. Filter list curation is a laborious process that does not scale well to a large number of sites or over time. In this paper, we introduce AutoFR, a reinforcement learning framework to fully automate the process of filter rule creation and evaluation for sites of interest. We design an algorithm based on multi-arm bandits to generate filter rules that block ads while controlling the trade-off between blocking ads and avoiding visual breakage. We test AutoFR on thousands of sites and we show that it is efficient: it takes only a few minutes to generate filter rules for a site of interest. AutoFR is effective: it generates filter rules that can block 86% of the ads, as compared to 87% by EasyList, while achieving comparable visual breakage. Furthermore, AutoFR generates filter rules that generalize well to new sites. We envision that AutoFR can assist the adblocking community in filter rule generation at scale.

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@inproceedings {287192,
author = {Hieu Le and Salma Elmalaki and Athina Markopoulou and Zubair Shafiq},
title = {{AutoFR}: Automated Filter Rule Generation for Adblocking},
booktitle = {32nd USENIX Security Symposium (USENIX Security 23)},
year = {2023},
isbn = {978-1-939133-37-3},
address = {Anaheim, CA},
pages = {7535--7552},
url = {},
publisher = {USENIX Association},
month = aug