Abstract:Traffic Classification is a method of categorizing the computer network traffic based on various features observed passively in the traffic into a number of traffic classes. Due to the rapid increase of different Internet application behaviors’, raised the need to disguise the applications for filtering, accounting, advertising, network designing etc. Many traditional methods like port based, packets based and some alternate methods based on machine learning approaches have been used for the classification process. Proposed a new traffic classification scheme to utilize the information among the correlated traffic flows generated by an application. Discretized statistical features are extracted and are used to represent the traffic flows. The removal of irrelevant and redundant features from the feature set is done by Correlation based feature selection with high class-specific correlation and low inter correlation. For the classification process Naïve Bayes with Discretization is used. The proposed scheme is compared with three other Bayesian models. The experimental evaluation show that NBD outperforms the other methods even in the case of a small supervised training samples.