P2P Traffic Identification Technique

Abstract

Accurate traffic classification for different P2P applications is fundamental to numerous network activities, from security monitoring, capacity planning and provisioning to service differentiation. However, current P2P applications use dynamic port numbers, HTTP masquerading and inaccessible payload to prevent being identified. The paper proposed an accurate P2P identification system using Decision Tree algorithms (J48 and REPTree) on the basis of effective feature selection. The experimental results show that our scheme is of better accuracy, less computational complexity and it is robust enough to deal with the unknown P2P traffic. With the merits, the scheme can suit the real-time active detection environment, such as monitoring network attacks camouflaged with P2P traffic and service differentiation.