Evaluation of Rough Set Theory Based Network Traffic Data Classifier Using Different Discretization Method
Keywords:
Classification, cuts, discretization, network traffic, rough set theoryAbstract
In information security, intrusion detection is a
challenging task for which designing of an efficient classifier is most important. In the paper, network traffic data is classified using rough set theory where discretization of data is a necessary preprocessing step. Different discretization methods are available and selection of one has great impact on classification
accuracy, time complexity and system adaptability. Three discretization methods are applied on
continuous KDD network data namely, rough set exploration system (RSES), supervised and unsupervised discretization methods to evaluate the classifier accuracy. It has been observed that supervised discretization yields best accuracy for rough set classification and provides system adaptability.
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