10 bits of surprise: Detecting malicious users with minimum information

Reza Zafarani, Huan Liu

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

19 Scopus citations

Abstract

Malicious users are a threat to many sites and defending against them demands innovative countermeasures. When malicious users join sites, they provide limited information about themselves. With this limited information, sites can find it difficult to distinguish between a malicious user and a normal user. In this study, we develop a methodology that identifies malicious users with limited information. As information provided by malicious users can vary, the proposed methodology utilizes minimum information to identify malicious users. It is shown that as little as 10 bits of information can help greatly in this challenging task. The experiments results verify that this methodology is effective in identifying malicious users in the realistic scenario of limited information availability.

Original languageEnglish (US)
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
PublisherAssociation for Computing Machinery
Pages423-431
Number of pages9
Volume19-23-Oct-2015
ISBN (Print)9781450337946
DOIs
StatePublished - Oct 17 2015
Event24th ACM International Conference on Information and Knowledge Management, CIKM 2015 - Melbourne, Australia
Duration: Oct 19 2015Oct 23 2015

Other

Other24th ACM International Conference on Information and Knowledge Management, CIKM 2015
CountryAustralia
CityMelbourne
Period10/19/1510/23/15

Keywords

  • Behavior analysis
  • Information verification
  • Malicious user detection
  • Minimum information

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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  • Cite this

    Zafarani, R., & Liu, H. (2015). 10 bits of surprise: Detecting malicious users with minimum information. In International Conference on Information and Knowledge Management, Proceedings (Vol. 19-23-Oct-2015, pp. 423-431). Association for Computing Machinery. https://doi.org/10.1145/2806416.2806535