Traffic Classification Using Machine Learning Algorithms in Practical Network Monitoring Environments 


Vol. 33,  No. 8, pp. 707-718, Aug.  2008


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  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.

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  Cite this article

[IEEE Style]

K. B. Jung, M. J. Choi, M. S. Kim, Y. J. Won, J. W. Hong, "Traffic Classification Using Machine Learning Algorithms in Practical Network Monitoring Environments," The Journal of Korean Institute of Communications and Information Sciences, vol. 33, no. 8, pp. 707-718, 2008. DOI: .

[ACM Style]

Kwang Bon Jung, Mi Jung Choi, Myung Sup Kim, Young J. Won, and James W. Hong. 2008. Traffic Classification Using Machine Learning Algorithms in Practical Network Monitoring Environments. The Journal of Korean Institute of Communications and Information Sciences, 33, 8, (2008), 707-718. DOI: .

[KICS Style]

Kwang Bon Jung, Mi Jung Choi, Myung Sup Kim, Young J. Won, James W. Hong, "Traffic Classification Using Machine Learning Algorithms in Practical Network Monitoring Environments," The Journal of Korean Institute of Communications and Information Sciences, vol. 33, no. 8, pp. 707-718, 8. 2008.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880