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Traffic Classification Using Machine Learning Algorithms in Practical Network Monitoring Environments  

Jung, Kwang-Bon (포항공과대학교 컴퓨터공학과)
Choi, Mi-Jung (포항공과대학교 컴퓨터공학과)
Kim, Myung-Sup (고려대학교 컴퓨터정보학과)
Won, Young-J. (포항공과대학교 컴퓨터공학과)
Hong, James W. (포항공과대학교 컴퓨터공학과)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.33, no.8B, 2008 , pp. 707-718 More about this Journal
Abstract
The methodology of classifying traffics is changing from payload based or port based to machine learning based in order to overcome the dynamic changes of application's characteristics. However, current state of traffic classification using machine learning (ML) algorithms is ongoing under the offline environment. Specifically, most of the current works provide results of traffic classification using cross validation as a test method. Also, they show classification results based on traffic flows. However, these traffic classification results are not useful for practical environments of the network traffic monitoring. This paper compares the classification results using cross validation with those of using split validation as the test method. Also, this paper compares the classification results based on flow to those based on bytes. We classify network traffics by using various feature sets and machine learning algorithms such as J48, REPTree, RBFNetwork, Multilayer perceptron, BayesNet, and NaiveBayes. In this paper, we find the best feature sets and the best ML algorithm for classifying traffics using the split validation.
Keywords
Traffic classification; algorithm;
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