Abstract—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.
Index Terms—Classification, cuts, discretization, network traffic, rough set theory.
N. Sengupta is with University College of Bahrain, P O Box 55040, Manama, Bahrain (e-mail: email@example.com).
J. Sil is with Bengal Engineering and Science University Shibpur, P. O. Botanic Garden, Howrah, West Bengal, Pin 711103 (e-mail: firstname.lastname@example.org).
Cite: Nandita Sengupta and Jaya Sil, "Evaluation of Rough Set Theory Based Network Traffic Data Classifier Using Different Discretization Method," International Journal of Information and Electronics Engineering vol. 2, no. 3, pp. 338-341, 2012.