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Bashir, Muhammad Ahmad; Arshad, Sajjad; Wilson, Christo

doi:10.1145/2987443.2987469

Cited by 8 publications

(1 citation statement)

References 11 publications

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“…They also reported that the whiltelisting process is flawed because "acceptable" ads are often disliked by users due to their intrusiveness and misleading resemblance to page content. In fact, the whitelisting of deceptive ads from content recommendation networks such as Taboola and Outbrain [17] has been quite controversial [3]. Pujol et al [60] showed that most ABP users do not opt-out of the Acceptable Ads whitelist despite these issues.…”

Section: A Whitelistingmentioning

confidence: 99%

CV-Inspector: Towards Automating Detection of Adblock Circumvention

Le¹,

Markopoulou²,

Shafiq³

2021

Proceedings 2021 Network and Distributed System Security Symposium

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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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Section: A Whitelistingmentioning

confidence: 99%