Authorship analysis in cybercrime investigation

Rong Zheng, Yi Qin, Zan Huang, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingChapter

63 Scopus citations

Abstract

Criminals have been using the Internet to distribute a wide range of illegal materials globally in an anonymous manner, making criminal identity tracing difficult in the cybercrime investigation process. In this study we propose to adopt the authorship analysis framework to automatically trace identities of cyber criminals through messages they post on the Internet. Under this framework, three types of message features, including style markers, structural features, and content-specific features, are extracted and inductive learning algorithms are used to build feature-based models to identify authorship of illegal messages. To evaluate the effectiveness of this framework, we conducted an experimental study on data sets of English and Chinese email and online newsgroup messages. We experimented with all three types of message features and three inductive learning algorithms. The results indicate that the proposed approach can discover real identities of authors of both English and Chinese Internet messages with relatively high accuracies.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsHsinchun Chen, Daniel D. Zeng, Therani Madhusudan, Richard Miranda, Jenny Schroeder, Chris Demchak
PublisherSpringer-Verlag
Pages59-73
Number of pages15
ISBN (Print)354040189X, 9783540401896
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2665
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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