DocumentCode :
2234754
Title :
The identification for P2P Thunder traffic based on deep flow identification
Author :
Jie Liu ; Fang Liu ; Dazhong He
Author_Institution :
Beijing Key Lab. of Network Syst. Archit. & Convergence, Beijing Univ. of Posts & Telecommun., Beijing, China
fYear :
2012
fDate :
Oct. 30 2012-Nov. 1 2012
Firstpage :
504
Lastpage :
507
Abstract :
Network traffic analysis and identification play important roles in network traffic monitoring. The network applications are the root causes to generate network traffic and network behavior. Network traffic analysis and identification is the basis of other network problems, which provides ISP an effective basis to control and distinguish the network traffic. With the popularity of Internet, "download" has become one of the most important parts of the domestic Internet users. As more and more resources, there is more and more enthusiastic discussion of the download tools. This paper takes one of the most popular download applications Thunder (also called Xunlei) for example, indicating the significance of the flow-based statistical network traffic classification and identification. Through the characteristics extraction, we can identify the Thunder traffic with high accuracy, and at the same time, the time cost in the experiment is reduced a lot.
Keywords :
Internet; telecommunication traffic; ISP; P2P Thunder traffic; Xunlei; deep flow identification; domestic Internet users; flow-based statistical network traffic classification; flow-based statistical network traffic identification; network behavior; network traffic analysis; network traffic monitoring; Accuracy; Character recognition; Classification algorithms; Inspection; Internet; Telecommunication traffic; Training; Characteristics extraction; DFI; Deep flow inspection; Thunder; Traffic identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cloud Computing and Intelligent Systems (CCIS), 2012 IEEE 2nd International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-1855-6
Type :
conf
DOI :
10.1109/CCIS.2012.6664225
Filename :
6664225
Link To Document :
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