JOURNAL OF COMMUNICATIONS (JCM)
ISSN : 1796-2021
Volume : 1    Issue : 7    Date : November/December 2006

Identification and Analysis of Peer-to-Peer Traffic
Marcell Perényi, Trang Dinh Dang, András Gefferth, and Sándor Molnár
Page(s): 36-46
Full Text:
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Abstract
Recent measurement studies report that a significant portion of Internet traffic is unknown. It is very
likely that the majority of the unidentified traffic originates from peer-to-peer (P2P) applications.
However, traditional techniques to identify P2P traffic seem to fail since these applications usually
disguise their existence by using arbitrary ports. In addition to the identification of actual P2P traffic,
the characteristics of that type of traffic are also scarcely known.
The main purpose of this paper is twofold. First, we propose a novel identification method to reveal
P2P traffic from traffic aggregation. Our method does not rely on packet payload so we avoid the
difficulties arising from legal, privacy-related, financial and technical obstacles. Instead, our method
is based on a set of heuristics derived from the robust properties of P2P traffic. We demonstrate our
method with current traffic data obtained from one of the largest Internet providers in Hungary. We
also show the high accuracy of the proposed algorithm by means of a validation study.
Second, several results of a comprehensive traffic analysis study are reported in the paper. We
show the daily behavior of P2P users compared to the non-P2P users. We present our important
finding about the almost constant ratio of the P2P and total number of users. Flow sizes and holding
times are also analyzed and results of a heavy-tail analysis are described. Finally, we discuss the
popularity distribution properties of P2P applications. Our results show that the unique properties of
P2P application traffic seem to fade away during aggregation and characteristics of the traffic will be
similar to that of other non-P2P traffic aggregation.

Index Terms
Peer-to-peer, identification, traffic analysis, heuristics