• DocumentCode
    589258
  • Title

    An Artificial Immune System Based on Holland´s Classifier as Network Intrusion Detection

  • Author

    Randrianasolo, Arisoa S. ; Pyeatt, Larry D.

  • Author_Institution
    Sch. of Comput. & Inf., Lipscomb Univ., Nashville, TN, USA
  • Volume
    1
  • fYear
    2012
  • fDate
    12-15 Dec. 2012
  • Firstpage
    504
  • Lastpage
    507
  • Abstract
    In this paper, an Artificial Immune System based on Hollandâs Classifier is proposed as a new method for network intrusion detection. This paper is not aimed to provide a comparative study but to give more understanding on the feasibility of combining Artificial Immune System and Hollandâs Classifier to detect network intrusion. This new Artificial Immune System, named AIS-CS, can attain higher than 90% intrusion detection with a false negative percentage below 10% and a fairly low false positive rate on a network composed of 50 regular nodes and 50 intruders. The experiments appear to suggest that the best performance can be found by setting the tolerization and the simulation parameters differently. Since there are numerous parameters involved, more experiments need to be performed to further measure this Holland classifier based Artificial Immune System.
  • Keywords
    artificial immune systems; computer network security; pattern classification; AIS-CS; Holland classifier; artificial immune system; false negative; false positive rate; intruders; network intrusion detection; regular nodes; simulation parameters; Biological information theory; Computers; Detectors; Genetic algorithms; Immune system; Intrusion detection; USA Councils; Artificial Immune System; Holland´s Classifier; Intrusion Detection System;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Applications (ICMLA), 2012 11th International Conference on
  • Conference_Location
    Boca Raton, FL
  • Print_ISBN
    978-1-4673-4651-1
  • Type

    conf

  • DOI
    10.1109/ICMLA.2012.92
  • Filename
    6406663