DocumentCode :
2842788
Title :
Online traffic classification based on sub-flows
Author :
de A Ribeiro, Victor Pasknel ; Filho, Raimir Rolanda ; Maia, José Everardo Bessa
Author_Institution :
Master´´s Course in Appl. Comput. Sci., Univ. of Fortaleza (UNIFOR), Fortaleza, Brazil
fYear :
2011
fDate :
23-27 May 2011
Firstpage :
415
Lastpage :
421
Abstract :
Traffic classification by application class provides useful information for various tasks of network engineering and administration. However, offline classification of flows has limited its practical application to auditing tasks, long-term planning and other analytical issues. Therefore, research on traffic classification now moves towards the search for accurate and efficient methods of classification in order to meet online tasks such as traffic monitoring and shaping and other specific-application operations. In this work we apply the One-Against-All Approach (OAA) for two online classification strategies based on statistical features of TCP sub-flows. One uses the first N packets of the bi-directional TCP session and the other applies to sub-flows of the N packets starting at a random position in the flow. In our variant of the OAA approach, the problem of classifying an object in one of M classes is reduced to M binary classification problems with an associated decision rule, with each of them possibly using a different subset of features and sub-flow size. We investigated the effect of variation in the amount of N on the results of classification and the smaller set of variables in each of the above problems. This study used the Naïve Bayes classifier.
Keywords :
Bayes methods; IP networks; computer network security; pattern classification; M binary classification problems; Naïve Bayes classifier; OAA; TCP; network administration; network engineering; one-against-all approach; online traffic classification; statistical features; subflows; traffic monitoring; traffic shaping; Browsers; IP networks; Internet; Phase measurement; Postal services; Training; Online traffic classification; one-against-all classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Integrated Network Management (IM), 2011 IFIP/IEEE International Symposium on
Conference_Location :
Dublin
Print_ISBN :
978-1-4244-9219-0
Electronic_ISBN :
978-1-4244-9220-6
Type :
conf
DOI :
10.1109/INM.2011.5990541
Filename :
5990541
Link To Document :
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