Hidden Markov model based P2P flow identification technique - Bo XU; Ming CHEN; Xiang-lin WEI (Tongxin xuebao)
To identify various P2P flows accurately in real-time,a hidden Markov model(HMM)based P2P flow identification technique was proposed.This approach made use of packet size,inter-arrival time and arrival order to construct flow identification model,in which discrete random variable was used to depict the characteristics of HMM state.A framework called HMM-FIA was proposed,which could identify various P2P flows simultaneously.Meanwhile,the algorithm for selecting the number of HMM state was designed.In a controllable experimental circumstance in the campus network,HMM-FIA was utilized to identify P2P flows and was compared with other identification methods.The results show that discrete random variable can decrease the model constructing time and improve the time-cost and accuracy in identifying unknown flows,HMM-FIA can correctly identify the packet flows produced by various P2P protocols and it can be adaptive to different network circumstance.