A Near Real-time IP Traffic Classification Using Machine Learning
Kuldeep Singh, Sunil Agrawal, Balwinder Singh Sohi · International Journal of Intelligent Systems and Applications · 2013
With drastic increase in internet traffic over last few years due to increase in nu mber of internet users, IP traffic classification has gained significant importance for research commun ity as well as various internet service providers for optimization of their network performance and for govern mental intelligence organizations.Today, traditional IP traffic classification techniques such as port number and payload based direct packet inspection techniques are rarely used because of use of dynamic port number instead of wellknown port nu mber in packet headers and various cryptographic techniques which inhibit inspection of packet payload.Current trends are use of machine learning (M L) techniques for IP traffic classification.In this research paper, a real time internet traffic dataset has been developed using packet capturing tool for 2 second packet capturing duration and other datasets have been developed by reducing number of features of 2 second duration dataset using Correlation and Consistency based Feature Selection (FS) A lgorith ms .Then, five M L algorithms M LP, RBF, C4.5, Bayes Net and Naï ve Bayes are emp loyed for IP traffic classification with these datasets.This experimental analysis shows that Bayes Net is an effective M L technique for near real time and online IP traffic classification with reduction in packet capture duration and reduction in number of features characterizing each application samp le with Correlat ion based FS Algorithm.