DocumentCode :
650391
Title :
P2P traffic identification research based on the SVM
Author :
Du Jiang ; Long Tao
Author_Institution :
Dept. of Commun. & Inf. Eng., Chongqing Univ. of Posts & Telecommun., Chongqing, China
fYear :
2013
fDate :
16-18 May 2013
Firstpage :
683
Lastpage :
686
Abstract :
This paper first introduces the advantages and disadvantages of all kinds of P2P traffic identification method especially machine learning traffic identification method, and then puts forward a method of P2P traffic identification model based on SVM. We select three characteristics including the change of the mean square value of P2P traffic data packet size, the time of average flow duration and the ratio of the IP address number and port number to identify the network flow. The experimental results show that the method can effectively detect the P2P traffic of network flow.
Keywords :
learning (artificial intelligence); peer-to-peer computing; support vector machines; telecommunication traffic; IP address number; P2P traffic data packet size; P2P traffic identification research; SVM; machine learning traffic identification method; mean square value; network flow identification; port number; Flow characteristics; P2P; SVM; traffic identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wireless and Optical Communication Conference (WOCC), 2013 22nd
Conference_Location :
Chongqing
Print_ISBN :
978-1-4673-5697-8
Type :
conf
DOI :
10.1109/WOCC.2013.6676461
Filename :
6676461
Link To Document :
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