Analysis of VBR coded VoIP for traffic classification

Abstract

Classification of Voice over Internet Protocol (VoIP) traffic is important for network management operations. The media traffic, which carries the voice on Real-time Transport Protocol (RTP), is subjected to variation in transmitted packet sizes and content due to the usage of Variable Bit Rate (VBR) codecs. In the absence of session level information, the RTP header does not uniquely identify the VBR voice codecs defined as dynamic payload type. In this paper we present a method to classify VoIP traffic coded with three VBR codecs - iSAC, SILK and Speex. We first formulate features to characterize an RTP flow based on packet size and entropy values of the packet content. The features are used for classification of RTP traffic based on codec using machine learning techniques. The paper reports classification results using the three machine learning algorithms, namely 1-NN, C4.5 and Naive Bayes. The results show an accuracy of over 98% for offline classification with the reduced feature set. The paper also presents the performance of the classifiers with varying size of available traffic.