Identifying high quality carding services in underground economy using nonparametric supervised topic model

Weifeng Li, Junming Yin, Hsinchun Chen

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

2 Scopus citations

Abstract

Over the years, cybercriminals increasingly joined the underground economy to exchange malicious services for conducting data breaches crimes. As many service providers are rippers, most cybercriminals rely on a few high quality services. To this end, cybercriminals post customer reviews evaluating the purchase experience and the service quality. To identify high quality services, researchers face two major challenges - the cybercriminal-specific language and the scale of the underground economy. This study presents a text-mining-based system for identifying high quality services by analyzing customer reviews. A novel supervised topic model is designed to accommodate the heterogeneous and uncertain nature of customer reviews. We further designed a variational algorithm for model inference. Moreover, we collected real data from two underground economy forums for English-speaking and Russian-speaking cybercriminals as our research testbed. Our research contributes to the practice of understanding and mitigating underground economy by providing cybersecurity researchers and practitioners with actionable intelligence.

Original languageEnglish (US)
Title of host publication2016 International Conference on Information Systems, ICIS 2016
PublisherAssociation for Information Systems
ISBN (Electronic)9780996683135
StatePublished - 2016
Event2016 International Conference on Information Systems, ICIS 2016 - Dublin, Ireland
Duration: Dec 11 2016Dec 14 2016

Publication series

Name2016 International Conference on Information Systems, ICIS 2016

Other

Other2016 International Conference on Information Systems, ICIS 2016
Country/TerritoryIreland
CityDublin
Period12/11/1612/14/16

Keywords

  • Bayesian nonparametrics
  • Customer reviews
  • Service quality
  • Text mining
  • Topic modeling
  • Underground economy

ASJC Scopus subject areas

  • Information Systems

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