Network Traffic Classification techniques and comparative analysis using Machine Learning algorithms

Network Traffic Classification is a central topic nowadays in the field of computer science. It is a very essential task for Internet service providers (ISPs) to know which types of network applications flow in a network. Network Traffic Classification is the first step to analyze and identify different types of applications flowing in a network. Through this technique, internet service providers or network operators can manage the overall performance of a network. There are many methods traditional technique to classify internet traffic like Port Based, Pay Load Based and Machine Learning Based technique. The most common technique used these days is Machine Learning (ML) technique. Which is used by many researchers and got very effective accuracy results. In this paper, we discuss network traffic classification techniques step by step and real time internet data set is develop using network traffic capture tool, after that feature extraction tool is use to extract features from the capture traffic and then four machine learning classifiers Support Vector Machine, C4.5 decision tree, Naïve Bays and Bayes Net classifiers are applied. Experimental analysis shows that C4.5 classifiers gives very good accuracy result as compare to other classifies.