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The increasing popularity of adblockers has prompted online publishers to retaliate against adblock users by deploying anti-adblock scripts, which detect adblock users and bar them from accessing content unless they disable their adblocker. To circumvent antiadblockers, adblockers rely on manually curated anti-adblock filter lists for removing anti-adblock scripts. Anti-adblock filter lists currently rely on informal crowdsourced feedback from users to add/remove filter list rules. In this paper, we present the first comprehensive study of anti-adblock filter lists to analyze their effectiveness against anti-adblockers. Specifically, we compare and contrast the evolution of two popular anti-adblock filter lists. We show that these filter lists are implemented very differently even though they currently have a comparable number of filter list rules. We then use the Internet Archive’s Wayback Machine to conduct a retrospective coverage analysis of these filter lists on Alexa top-5K websites over the span of last five years. We find that the coverage of these filter lists has considerably improved since 2014 and they detect anti-adblockers on about 9% of Alexa top-5K websites. To improve filter list coverage and speedup addition of new filter rules, we also design and implement a machine learning based method to automatically
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CV-Inspector: Towards Automating Detection of Adblock Circumvention
The adblocking arms race has escalated over the last few years. An entire new ecosystem of circumvention (CV) services has recently emerged that aims to bypass adblockers by obfuscating site content, making it difficult for adblocking filter lists to distinguish between ads and functional content. In this paper, we investigate recent anti-circumvention efforts by the adblocking community that leverage custom filter lists. In particular, we analyze the anti-circumvention filter list (ACVL), which supports advanced filter rules with enriched syntax and capabilities designed specifically to counter circumvention. We show that keeping ACVL rules up-to-date requires expert list curators to continuously monitor sites known to employ CV services and to discover new such sites in the wild — both tasks require considerable manual effort. To help automate and scale ACVL curation, we develop CV-INSPECTOR, a machine learning approach for automatically detecting adblock circumvention using differential execution analysis. We show that CV-INSPECTOR achieves 93% accuracy in detecting sites that successfully circumvent adblockers. We deploy CV-INSPECTOR on top-20K sites to discover the sites that employ circumvention in the wild.We further apply CV-INSPECTOR to a list of sites that are known to utilize circumvention and are closely monitored by ACVL authors. We demonstrate that CV-INSPECTOR reduces the human labeling effort by 98%, which removes a major bottleneck for ACVL authors. Our work is the first large-scale study of the state of the adblock circumvention arms race, and makes an important step towards automating anti-CV efforts.
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- PAR ID:
- 10288855
- Date Published:
- Journal Name:
- Network and Distributed Systems Security (NDSS) Symposium 2021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.14722/ndss.2021.24055
