• DocumentCode
    2119946
  • Title

    Analysing Behaviours for Intrusion Detection

  • Author

    Mamalakis, George ; Diou, Christos ; Symeonidis, Andreas L.

  • Author_Institution
    School of Electrical and Computer Engineering, Aristotle Univ. of Thessaloniki, Greece
  • fYear
    2015
  • fDate
    8-12 June 2015
  • Firstpage
    2645
  • Lastpage
    2651
  • Abstract
    In this work, a Behaviour-based Intrusion Detection Model is suggested. The proposed model can be employed from a single host configuration to a distributed mixture of host-based and network-based Intrusion Detection Systems (IDSs). Unlike most state-of-the-art IDSs that rely on analysing lower-level, raw-data representations, our proposed architecture suggests to use higher-level notions -behaviours- instead; this way, the IDS is able to identify more sophisticated attacks. To assess our premise, a Behaviour-based IDS (BIDS) prototype has been designed and developed that scans file system data to identify attacks. BIDS achieves high detection rates with low corresponding false positive rates, superseding other state-of-the-art file system IDSs.
  • Keywords
    Clustering algorithms; Computers; Engines; Feature extraction; Generators; Internet of things; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communication Workshop (ICCW), 2015 IEEE International Conference on
  • Conference_Location
    London, United Kingdom
  • Type

    conf

  • DOI
    10.1109/ICCW.2015.7247578
  • Filename
    7247578