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Identification of P2P Traffic across the Networks: A Survey

M. Sadish Sendil, M. Kavitha, U. Kaleelurrahaman, Dr S. Karthik, Dr. N. Nagarajan


Problem statement: In the last years, we have seen that the use of P2P applications has increased significantly and currently they represent a significant portion of the Internet traffic. In consequence of this growth, P2P traffic characterization and identification are becoming increasingly important for network administrators and designers. Also increasing amount of P2P services not only consuming a lot of bandwidth but also influencing the performance of other business. Another difficulty in identification of traffic in P2P is that P2P applications have the ability to disguise their existence through the use of arbitrary ports and explicitly try to camouflage the original traffic in an attempt to go undetected. In this paper we have analyzed different type of identification methods and compared the efficiency in each method. Approaches: 1. P2P traffic identification using Cluster analysis which seek five traffic discriminators and applies cluster analysis to identify P2P traffic. 2. P2P traffic identification based on double layer characteristics which identify traffic based on flow characteristics and payload characteristics. 3. Transport layer heuristic identification in which P2P traffic flows are identified by analyzing the statistical properties of the flows and their behavior characteristics. 4. Payload-based IP traffic identification packet payloads are analyzed to determine whether they contain specific signatures of known applications. Results: After analyzing each model we find out that there are both advantages and limitation in it. We have simulated those models and compared with each other. Conclusion and Future work: With the widely adoption of the P2P, the method for the control and the management to the P2P applications has become a research hotspot. In this paper we analyse different methods to identify the traffic in P2P application. In future we try to build a new algorithm which combines the best capabilities of the analyzed approaches.


Cluster Analysis, Flow Characteristics, Payload Characteristics, Traffic Identificat

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