Comparison of performance for intrusion detection system using different rules of classification

Abstract

Classification is very important for designing intrusion detection system that classifies network traffic data. Broadly traffic data is classified as normal or anomaly. In the work classification performance using rules obtained by different methods are applied on network traffic and compared. Classifier is built based on rules of decision table, conjunctive rule, OneR, PART, JRip, NNge, ZeroR, BayesNet, Ridor from WEKA and using rough set theory. Classification performance is compared applying on KDD data set where the whole data set is divided into training and test data set. Rules are formed using training data set by different rule generation methods and later applied on test data set to calculate accuracy of classifiers. © Springer-Verlag 2011.