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Analysis of Defuzzification Methods for Network Intrusion Detection

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dc.contributor.author Hlaing, Thuzar
dc.date.accessioned 2019-07-12T04:29:46Z
dc.date.available 2019-07-12T04:29:46Z
dc.date.issued 2013-02-26
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/835
dc.description.abstract Fuzzy logic is appropriated for the intrusion detection problem because many quantitative features are involved in intrusion detection. Fuzzy logic system can handle simultaneously the numerical data and linguistic knowledge. The concept of linguistic variables is used to model the state of the system which is imprecise and uncertain. The purpose of this paper is to analyze the behavior of the intrusion detection on the KDD dataset using the five defuzzification methods. The result shows that the centroid and bisector methods can detect intrusion better than the other methods for intrusion detection. The experiments and evaluations of this paper were performed with the KDD Cup 99 intrusion detection dataset. Simulation results are demonstrated by using MATLAB. en_US
dc.language.iso en en_US
dc.publisher Eleventh International Conference On Computer Applications (ICCA 2013) en_US
dc.subject Fuzzy Logic en_US
dc.subject Defuzzification en_US
dc.subject Centroid en_US
dc.subject Bisector en_US
dc.subject Intrusion Detection en_US
dc.title Analysis of Defuzzification Methods for Network Intrusion Detection en_US
dc.type Article en_US

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