Hidden Markov model-based P2P flow identification: a hidden Markov model-based P2P flow identification method

Abstract

It is critical to identify various P2P flows accurately for managing P2P traffic as well as for ensuring network security. In this study, a hidden Markov model -based P2P flow identification (HMM-PFI) method is put forward, which makes use of packet size, the inter-arrival time and the arrival order of packets to build flow identification model, the packets in sample flow are associated with the states of hidden Markov model (HMM). Moreover, some discrete random variables are introduced to depict the characteristics of HMM state. The method dramatically decreases the time needed for building the model and improves the real-time and accuracy in identifying unknown flows. In HMM-PFI, various P2P flows can be identified simultaneously. Meanwhile, the algorithm for selecting the number of HMM state is designed. In a controllable experimental circumstance such as our campus network, HMM-PFI is utilised to identify P2P flows whereas several other identification methods are chosen as benchmark. The results show that HMM-PFI can correctly identify the packet flows produced by various P2P protocols and it is preferably adaptive to different network circumstance.