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CLC number: TP393

On-line Access: 2015-05-05

Received: 2014-07-22

Revision Accepted: 2014-12-12

Crosschecked: 2015-04-09

Cited: 2

Clicked: 3478

Citations:  Bibtex RefMan EndNote GB/T7714


Jie He


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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.5 P.391-403


Fine-grained P2P traffic classification by simply counting flows

Author(s):  Jie He, Yue-xiang Yang, Yong Qiao, Wen-ping Deng

Affiliation(s):  College of Computer, National University of Defense Technology, Changsha 410073, China; more

Corresponding email(s):   hejie@nudt.edu.cn, yyx@nudt.edu.cn, brave_jo@163.com, wpdeng.nudt@gmail.com

Key Words:  Traffic classification, Peer-to-peer (P2P), Fine-grained, Host-based

Jie He, Yue-xiang Yang, Yong Qiao, Wen-ping Deng. Fine-grained P2P traffic classification by simply counting flows[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(5): 391-403.

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DOI - 10.1631/FITEE.1400267

The continuous emerging of peer-to-peer (P2P) applications enriches resource sharing by networks, but it also brings about many challenges to network management. Therefore, P2P applications monitoring, in particular, P2P traffic classification, is becoming increasingly important. In this paper, we propose a novel approach for accurate P2P traffic classification at a fine-grained level. Our approach relies only on counting some special flows that are appearing frequently and steadily in the traffic generated by specific P2P applications. In contrast to existing methods, the main contribution of our approach can be summarized as the following two aspects. Firstly, it can achieve a high classification accuracy by exploiting only several generic properties of flows rather than complicated features and sophisticated techniques. Secondly, it can work well even if the classification target is running with other high bandwidth-consuming applications, outperforming most existing host-based approaches, which are incapable of dealing with this situation. We evaluated the performance of our approach on a real-world trace. Experimental results show that P2P applications can be classified with a true positive rate higher than 97.22% and a false positive rate lower than 2.78%.

This paper makes use of host based statistics to answer the question as to whether or not a host is running a peer to peer application. The idea of exploiting special flows for detecting specific P2P protocols/implementations is neat. The application of a clustering algorithm for automatically identifying special flows makes the approach flexibile and extensibile.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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