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Evolving optimised decision rules for intrusion detection using particle swarm paradigm

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  • Siva Sivatha Sindhu
  • S. Geetha
  • A. Kannan

Abstract

The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

Suggested Citation

  • Siva Sivatha Sindhu & S. Geetha & A. Kannan, 2012. "Evolving optimised decision rules for intrusion detection using particle swarm paradigm," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(12), pages 2334-2350.
  • Handle: RePEc:taf:tsysxx:v:43:y:2012:i:12:p:2334-2350
    DOI: 10.1080/00207721.2011.577244
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    References listed on IDEAS

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    1. Kjell Hausken, 2011. "Protecting complex infrastructures against multiple strategic attackers," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(1), pages 11-29.
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    Cited by:

    1. José Carlos Castillo & Davide Carneiro & Juan Serrano-Cuerda & Paulo Novais & Antonio Fernández-Caballero & José Neves, 2014. "A multi-modal approach for activity classification and fall detection," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(4), pages 810-824, April.
    2. Kuo-Hsiung Wang & Cheng-Dar Liou & Ya-Lin Wang, 2014. "Profit optimisation of the multiple-vacation machine repair problem using particle swarm optimisation," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(8), pages 1769-1780, August.

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