Feature Selection Technique Impact for Internet Traffic Classification Using Naïve Bayesian

Abstract

Feature selection technique has an important role for internet traffic classification. This technique will present more accurate data and more accurate internet traffic classification which will provide precise information for bandwidth optimization. One of the important considerations in the feature selection technique that should be looked into is how to choose the right features which can deliver better and more precise results for the classification process. This research will compare feature selection algorithms where the Internet traffic has the same correlation that could fit into the same class. Internet traffic dataset will be collected, formatted, classified and analyzed using Naïve Bayesian. Formerly, the Correlation Feature Selection (CFS) is used in the feature selection to find a collection of the best sub-sets data from the existing data but without the discriminant and principal of a body dataset. We plan to use Principal Component Analysis technique in order to find discriminant and principal feature for internet traffic classification. Moreover, this paper also studied the process to fit the features. The result also shows that the internet traffic classification using Naïve Bayesian and Correlation Feature Selection (CFS) have more than 90% accuracy while the classification accuracy reached 75% for feature selection using Principal Component Analysis (PCA).