Leveraging machine learning to improve unwanted resource filtering

Sruti Bhagavatula, Christopher Dunn, Chris Kanich, Minaxi Gupta, Brian Ziebart

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

Advertisements simultaneously provide both economic support for most free web content and one of the largest annoyances to end users. Furthermore, the modern advertisement ecosystem is rife with tracking methods which violate user privacy. A natural reaction is for users to install ad blockers which prevent advertisers from tracking users or displaying ads. Traditional ad blocking software relies upon hand-crafted filter expressions to generate large, unwieldy regular expressions matched against resources being included within web pages. This process requires a large amount of human overhead and is susceptible to inferior filter generation. We propose an alternate approach which leverages machine learning to bootstrap a superior classifier for ad blocking with less human intervention. We show that our classifier can simultaneously maintain an accuracy similar to the hand-crafted filters while also blocking new ads which would otherwise necessitate further human intervention in the form of additional handmade filter rules.

Original languageEnglish (US)
Title of host publicationAISec 2014 - Proceedings of the 2014 ACM Artificial Intelligent and Security Workshop, Co-located with CCS 2014
PublisherAssociation for Computing Machinery
Pages95-102
Number of pages8
EditionNovember
ISBN (Print)9781450331531
DOIs
StatePublished - Nov 7 2014
Externally publishedYes
Event2014 7th ACM Workshop Artificial Intelligence and Security, AISec 2014, Co-located with the ACM Conference on Computer and Communication Security, CCS 2014 - Scottsdale, United States
Duration: Nov 7 2014 → …

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
NumberNovember
Volume2014-November
ISSN (Print)1543-7221

Conference

Conference2014 7th ACM Workshop Artificial Intelligence and Security, AISec 2014, Co-located with the ACM Conference on Computer and Communication Security, CCS 2014
Country/TerritoryUnited States
CityScottsdale
Period11/7/14 → …

Keywords

  • Machine learning
  • Web privacy
  • Web security

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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