• DocumentCode
    126922
  • Title

    The effect of attribute pairings in intrusion detection

  • Author

    Milliken, Michael ; Yaxin Bi ; Galway, L.

  • Author_Institution
    Sch. of Comput. & Math., Univ. of Ulster, Newtownabbey, UK
  • fYear
    2014
  • fDate
    8-10 Sept. 2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    As Network Intrusions have become larger and more pervasive the methods of detection have changed, a number of systems use ensemble methods to improve upon results from single classifiers or algorithms. The solutions proposed in the literature achieve good results, which primarily focus on classification of Network Intrusions by tailoring classification algorithms and feature selection. However fewer studies focus on investigation of relation between pairs of attributes, such as IP address and Port, as a single attribute. This paper proposes an effect analysis of pairs of attributes in order to improve intrusion detection using an ensemble-based classification approach.
  • Keywords
    learning (artificial intelligence); security of data; attribute pairings; ensemble-based classification approach; feature selection; network intrusion detection; Algorithm design and analysis; Classification algorithms; Hidden Markov models; IP networks; Machine learning algorithms; Payloads; Ports (Computers); ensemble methods; intrusion detection; supervised machine learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence (UKCI), 2014 14th UK Workshop on
  • Conference_Location
    Bradford
  • Type

    conf

  • DOI
    10.1109/UKCI.2014.6930185
  • Filename
    6930185