• DocumentCode
    2315888
  • Title

    Applying graph-based anomaly detection approaches to the discovery of insider threats

  • Author

    Eberle, William ; Holder, Lawrence

  • Author_Institution
    Dept. of Comput. Sci., Tennessee Technol. Univ., Cookeville, TN
  • fYear
    2009
  • fDate
    8-11 June 2009
  • Firstpage
    206
  • Lastpage
    208
  • Abstract
    The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns. One important area of data mining is anomaly detection, but little work has been done in terms of detecting anomalies in graph-based data. In this paper we present graph-based approaches to uncovering anomalies in applications containing information representing possible insider threat activity: e-mail, cell-phone calls, and order processing.
  • Keywords
    data mining; graph theory; security of data; cell-phone call; data mining; e-mail; graph-based anomaly detection approach; Algorithm design and analysis; Application software; Computer science; Computer security; Data analysis; Data mining; Information analysis; Monitoring; Telecommunication traffic; Terrorism; anomaly detection; insider threat; minimum description length;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligence and Security Informatics, 2009. ISI '09. IEEE International Conference on
  • Conference_Location
    Dallas, TX
  • Print_ISBN
    978-1-4244-4171-6
  • Electronic_ISBN
    978-1-4244-4173-0
  • Type

    conf

  • DOI
    10.1109/ISI.2009.5137304
  • Filename
    5137304