Abstract
AbstractVoice and video over IP are becoming increasingly popular and represent the largest source of profits as consumer interest in online voice and video services increases, and as broadband deployments proliferate. In order to tap the potential profits that VoIP and IPTV offer, carrier networks have to efficiently and accurately manage and track the delivery of IP services. The traditional approach of using port numbers to classify traffic is infeasible due to the usage of dynamic port number. In this paper, we focus on a statistical pattern classification technique to identify multimedia traffic. Based on the intuitions that voice and video data streams show strong regularities in the packet inter‐arrival times (IATs) and the associated packet sizes when combined together in one single stochastic process, we propose a system, called VOVClassifier, for voice and video traffic classification. VOVClassifier is an automated self‐learning system that classifies traffic data by extracting features from frequency domain using Power Spectral Density (PSD) analysis and grouping features using Subspace Decomposition. We applied VOVClassifier to real packet traces collected from different network scenarios. Results demonstrate the effectiveness and robustness of our approach that is capable of achieving a detection rate of up to 100% for voice and 96.5% for video while keeping the false positive rate close to 0%. Copyright © 2009 John Wiley & Sons, Ltd.
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