Self-Learning Peer-to-Peer Traffic Classifier

Date Added: May 2009
Format: PDF

The popularity of a new generation of smart peer-to-peer applications has resulted in several new challenges for accurately classifying network traffic. In this paper, the authors propose a novel 2- stage p2p traffic classifier, called Self Learning Traffic Classifier (SLTC), that can accurately identify p2p traffic in high speed networks. The first stage classifies p2p traffic from the rest of the network traffic, and the second stage automatically extracts application payload signatures to accurately identify the p2p application that generated the p2p flow. For the first stage, they propose a fast, light-weight algorithm called Time Correlation Metric (TCM), that exploits the temporal correlation of flows to clearly separate Peer-to-Peer (p2p) traffic from the rest of the traffic.