A comparative performance analysis on network traffic classification using supervised learning algorithms

Network traffic classification has become vital due to the increased flow of web traffic from services like HTTP, FTP, and SMTP etc. The idea of Network traffic classification is to categorize the network traffic and regulate the flow of data in the network there by monitoring the traffic. In this paper, a comparative analysis of different supervised learning algorithms like Ensemble learning methods, Trees, Bayes method are done and results are examined. To select the most reliable features, wrapper method-classifier subset evaluation feature selection method is used. From the analysis results of different algorithms, decorate algorithm which belongs to the ensemble classifiers performs better than other algorithms.