Topology-Based Traffic Identification Method with Heuristic Rules

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Abstract:

Traffic identification using statistic features of traffic flows has attracted a great deal of interest. One challenging issue for these methods is that they ignore the short flows containing just 2-3 packets, for statistic features of short flows are mainly insignificant or meaningless. A topology-based method combining heuristic rules and deep-in packet inspection is proposed to identify the application types of traffic flows. The experiment results demonstrate that the method can get higher precisions and similar recalls compared to deep-in packet inspection method.

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Periodical:

Advanced Materials Research (Volumes 588-589)

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1427-1430

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November 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] A. Callado, C. Kamienski, G. Szabo, B. P. Gero: A Survey on Internet Traffic Identification, IEEE Communications Surveys and Tutorials vol. 11 (2009), pp.37-52.

DOI: 10.1109/surv.2009.090304

Google Scholar

[2] M. M. Freire, D. A. Carvalho and M. Pereira: Detection of Encrypted Traffic in eDonkey Network through Application Signatures, in Advances in P2P Systems Conference (2009), pp.174-179.

DOI: 10.1109/ap2ps.2009.35

Google Scholar

[3] T. T. T. Nguyen and G. Armitage: A survey of techniques for internet traffic classification using machine learning Communications Surveys & Tutorials, IEEE, vol. 10 (2008), pp.56-76.

DOI: 10.1109/surv.2008.080406

Google Scholar

[4] T. Auld, A. W. Moore and S. F. Gull: Bayesian Neural Networks for Internet Traffic Classification Neural Networks, IEEE Transactions on, vol. 18 (2007), pp.223-239.

DOI: 10.1109/tnn.2006.883010

Google Scholar

[5] L. Bernaille and R. Teixeira: Implementation issues of early application identification, in Sustainable Internet, edtied by S. Fdida BERLIN: SPRINGER-VERLAG, (2007).

Google Scholar

[6] T. Karagiannis, K. Papagiannaki and M. Faloutsos: BLINC: Multilevel traffic classification in the dark, Computer Communications Review, vol. 35 (2005), pp.229-240.

DOI: 10.1145/1090191.1080119

Google Scholar

[7] G. Dewaele, Y. Himura, P. Borgnat, K. Fukuda: Unsupervised host behavior classification from connection patterns, " International Journal of Network Management, vol. 25 (2010), pp.317-337.

DOI: 10.1002/nem.750

Google Scholar