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Insider Threat Detection Using Stream Mining and Graph Mining

P. Parveen,Jonathan Evans,B. Thuraisingham,Kevin W. Hamlen,L. Khan

Published 2011 in 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing

ABSTRACT

Evidence of malicious insider activity is often buried within large data streams, such as system logs accumulated over months or years. Ensemble-based stream mining leverages multiple classification models to achieve highly accurate anomaly detection in such streams even when the stream is unbounded, evolving, and unlabeled. This makes the approach effective for identifying insider threats who attempt to conceal their activities by varying their behaviors over time. This paper applies ensemble-based stream mining, unsupervised learning, and graph-based anomaly detection to the problem of insider threat detection, demonstrating that the ensemble-based approach is significantly more effective than traditional single-model methods.

PUBLICATION RECORD

  • Publication year

    2011

  • Venue

    2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing

  • Publication date

    2011-10-01

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.1109/PASSAT/SocialCom.2011.211
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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