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.
The transition of voice communication from public switched telephone networks (PSTN) to IP network has offered numerous advantages, at the same time, myriad of security threats. Common among these threats is DoS attacks which was not possible in PSTN with closed architecture. This paper examines the denial-of-service (DoS) attacks on session initiation protocol (SIP) server using SIP particularly with REGISTER messages, focusing on the design of a framework to protect SIP server from such attacks. The proposed scheme introduces an intermediate server between SIP server and the User Agents, which is used to filter out attacks.
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