Abstract
Modern network traffic classification approaches apply machine learning techniques to statistical flow properties, allowing accurate classification even when traditional approaches fail. We base our approach to the task on a state-of-the-art semi-supervised classifier to identify known and unknown flows with little labelled training data. We propose a new algorithm for mapping clusters to classes to target classes that were previously difficult to classify. We also apply alternative statistical features. We find our approach has an accuracy of 95.10 %, over 17 % above the technique on which it is based. Additionally, our approach improves the classification performance on every class.
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References
Karagiannis, T., Broido, A., Faloutsos, M.: Transport layer identification of P2P traffic. In: ACM SIGCOMM Conference on Internet Measurement, pp. 121–134 (2004)
Erman, J., et al.: Offline/realtime traffic classification using semi-supervised learning. Perform. Eval. 64(9), 1194–1213 (2007)
Williams, N., Zander, S., Armitage, G.: Evaluating machine learning algorithms for automated network application identification. Center for Advanced Internet Architectures (CAIA), Technical Report B, 60410 (2006)
Zhang, J., Chen, C., Xiang, Y., Zhou, W., Vasilakos, A.V.: An effective network traffic classification method with unknown flow detection. IEEE Trans. Netw. Serv. Manage. 10(2), 133–147 (2013)
Erman, J., et al.: Offline/realtime traffic classification using semi-supervised learning. Perform. Eval. 64(9), 1194–1213 (2005)
Auld, T., Moore, A.W., Gull, S.F.: Bayesian neural networks for internet traffic classification. IEEE Trans. Neural Networks 18(1), 223–239 (2007)
McGregor, A., Hall, M., Lorier, P., Brunskill, J.: Flow clustering using machine learning techniques. In: Barakat, C., Pratt, I. (eds.) PAM 2004. LNCS, vol. 3015, pp. 205–214. Springer, Heidelberg (2004)
Erman, J., Arlitt, M., Mahanti, A.: Traffic classification using clustering algorithms. In: SIGCOMM Workshop on Mining Network Data, pp. 281–286 (2006)
Nguyen, T.T., Armitage, G.: A survey of techniques for internet traffic classification using machine learning. IEEE Comm. Surv. Tutorials 10(4), 56–76 (2008)
Scikit-Learn.: http://scikit-learn.org/stable/modules/ensemble.html (as of March 2016)
NetMate.: http://sourceforge.net/projects/netmate-meter/ (as of March 2016)
Williams, N., Zander, S., Armitage, G.: A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. ACM SIGCOMM Comput. Commun. Rev. 36(5), 5–16 (2006)
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Glennan, T., Leckie, C., Erfani, S.M. (2016). Improved Classification of Known and Unknown Network Traffic Flows Using Semi-supervised Machine Learning. In: Liu, J., Steinfeld, R. (eds) Information Security and Privacy. ACISP 2016. Lecture Notes in Computer Science(), vol 9723. Springer, Cham. https://doi.org/10.1007/978-3-319-40367-0_33
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DOI: https://doi.org/10.1007/978-3-319-40367-0_33
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