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Date of Publication:2013-01-01

Journal:哈尔滨工程大学学报

Affiliation of Author(s):电子信息与电气工程学部

Issue:5

Page Number:653-657,661

ISSN No.:1006-7043

Abstract:In this study a method for P2P traffic identification was proposed based on utilizing semi-supervised affinity propagation clustering aimed at accurately identifying P2P traffic with as few labeled samples as possible. Firstly, a small amount of samples were labeled. Secondly, the labeled as well as the unlabeled samples were configured with different 'preference' parameters, which made it more likely for the labeled samples to become exemplars, as opposed to the unlabeled samples. Thirdly, all samples were clustered by a weighted message passing. Finally, P2P traffic was identified by the preset ' marks-category' mapping rules based on the clustering results. The influence of the ' preference' parameter and weighted message passing on the identification was examined. Experimental results show the true positive rate (TPrate) was above 90% and the false positive rate (FPrate) was below 3% when the proportion of labeled samples was 5%; the TPrate was above 95% with a maximum of 98% while the FPrate was below 1% when the proportion of labeled samples increased to 15% and above; and increase of the proportion of labeled samples may result to performance improvement of the proposed method.

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