Automated detection and fingerprinting of censorship block pages

Ben Jones, Tzu Wen Lee, Nick Feamster, Phillipa Gill

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

12 Scopus citations

Abstract

One means of enforcing Web censorship is to return a block page, which informs the user that an attempt to access a webpage is unsuccessful. Detecting block pages can provide a more complete picture of Web censorship, but automatically identifying block pages is difficult because Web content is dynamic, personalized, and may even be in different languages. Previous work has manually detected and identified block pages, which is difficult to reproduce; it is also time-consuming, which makes it difficult to perform continuous, longitudinal studies of censorship. This paper presents an automated method both to detect block pages and to fingerprint the filtering products that generate them. Our automated method enables continuous measurements of block pages; we found that our methods successfully detect 95% of block pages and identify five filtering tools, including a tool that had not been previously identified "in the wild".

Original languageEnglish (US)
Title of host publicationIMC 2014 - Proceedings of the 2014 ACM
PublisherAssociation for Computing Machinery
Pages299-304
Number of pages6
ISBN (Electronic)9781450332132
DOIs
StatePublished - Nov 5 2014
Event2014 ACM Internet Measurement Conference, IMC 2014 - Vancouver, Canada
Duration: Nov 5 2014Nov 7 2014

Publication series

NameProceedings of the ACM SIGCOMM Internet Measurement Conference, IMC

Other

Other2014 ACM Internet Measurement Conference, IMC 2014
CountryCanada
CityVancouver
Period11/5/1411/7/14

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

Keywords

  • Censorship
  • Internet measurement

Cite this

Jones, B., Lee, T. W., Feamster, N., & Gill, P. (2014). Automated detection and fingerprinting of censorship block pages. In IMC 2014 - Proceedings of the 2014 ACM (pp. 299-304). (Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC). Association for Computing Machinery. https://doi.org/10.1145/2663716.2663722