Identifying top sellers in underground economy using deep learning-based sentiment analysis

Weifeng Li, Hsinchun Chen

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

34 Scopus citations

Abstract

The underground economy is a key component in cyber carding crime ecosystems because it provides a black marketplace for cyber criminals to exchange malicious tools and services that facilitate all stages of cyber carding crime. Consequently, black market sellers are of particular interest to cybersecurity researchers and practitioners. Malware/carding sellers are critical to cyber carding crime since using malwares to skim credit/debit card information and selling stolen information are two major steps of conducting such crime. In the underground economy, the malicious product/service quality is reflected by customers' feedback. In this paper, we present a deep learning-based framework for identifying top malware/carding sellers. The framework uses snowball sampling, thread classification, and deep learning-based sentiment analysis to evaluate sellers' product/service quality based on customer feedback. The framework was evaluated on a Russian carding forum and top malware/carding sellers from it were identified. Our framework contributes to underground economy research as it provides a scalable and generalizable framework for identifying key cybercrime facilitators.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE Joint Intelligence and Security Informatics Conference, JISIC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages64-67
Number of pages4
ISBN (Electronic)9781479963645
DOIs
StatePublished - Dec 4 2014
Event2014 IEEE Joint Intelligence and Security Informatics Conference, JISIC 2014 - The Hague, Netherlands
Duration: Sep 24 2014Sep 26 2014

Publication series

NameProceedings - 2014 IEEE Joint Intelligence and Security Informatics Conference, JISIC 2014

Other

Other2014 IEEE Joint Intelligence and Security Informatics Conference, JISIC 2014
CountryNetherlands
CityThe Hague
Period9/24/149/26/14

Keywords

  • carding crime
  • cybersecurity
  • deep learning
  • sentiment analysis
  • top sellers
  • underground economy

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Software

Fingerprint Dive into the research topics of 'Identifying top sellers in underground economy using deep learning-based sentiment analysis'. Together they form a unique fingerprint.

Cite this