Intrusion Detection Based on Hybrid CatfishPSO-LSSVM Feature Selection
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    Abstract:

    The main issue of Intrusion detection systems is large computation, feature selection was introduced to solve the problem. According to the shortcomings of existing methods, this paper uses improved Particle Swarm Optimization to search optimal feature subset, proposes a feature selection method based on hybrid CatfishPSO and Least Square Support Vector Machine, uses combined CatfishBPSO and CatfishPSO to select feature subset and optimize the parameters of LSSVM simultaneously, and build a Intrusion detection model based on the feature selection method above. Experiments on KDD Cup 99 show that the model has a good detection performance.

    Reference
    1 Sung AH, Mukkamala S. Identifying important features forintrusion detection using support vector machines andneural networks. Proc. of the 2003 International Symposium on Applications and the Internet Technology.IEEE Computer Society Press, 2003: 209-216.
    2 Stein G, Chen B, Wu AS, Hua KA. Decision tree classifier fornetwork intrusion detection with GA-based feature selection,Proc. of the 43rd ACM Southeast Regional Conference.Kennesaw, Georgia: 2005,2:136-141.
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    8 KDD Cup 99 Datasets http://kdd.ics.uci.edu/databases/kddc-up99/kddcup99.html:1999.
    9 Mukkamala S, Sung A, Abraham A. Intrusion detectionusing an ensemble of Intelligent paradigms. Journal ofNetwork and Computer Applications, 2005,28(2):167-182.
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王卫平,唐志煦.基于混合CatfishPSO-LSSVM 特征选择的入侵检测.计算机系统应用,2012,21(1):85-89

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History
  • Received:May 19,2011
  • Revised:June 29,2011
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