Internet Traffic Classification Using LPC Cepstrum

Abstract

Internet traffic volumes continue to increase rapidly with the spread of the broadband access and the increase in internet backbone capacities. Moreover, the applications provided over the internet have become more diverse. Adequately supporting the QoS requirements for these diverse applications requires measuring traffic volumes by application type(file transfer, broadcast and so on). Therefore, methods for classifying applications become extremely important for multi QoS environment. In some application traffic classification methods, the applications are analyzed on the basis of statistics for the traffic characteristics, such as the packet inter arrival time and packet size.In this paper, we propose application traffic classification method applying LPC cepstrum. The proposed method enables to treat the traffic data as a time series signal and to separate spectral envelope information and spectral fine structure information from spectrum. We show that it is effective to use the LPC cepstrum to classify internet traffic by comparing LPC cepstrum with flow and raw data in this paper.

Analysis and practicality of traffic flow classification method using binned time-series data

“…In recent years, a number of researchers have studied about ways to classify traffic data into types of applications. These studies include a clustering technique based on swarm intelligence to classify accurately with small computational complexity [2], the classification technique with LPC (Linear Predictive Coding) cepstrum to reduce influence of capture time and environment [3], and the method of application identification based on characteristic change by encryption of traffic to classify the mixed traffic data of encrypted/normal communications [4].…”