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Accurately classifying and identifying wireless network traffic associated with various applications, such as Web, VoIP, and VoD, is a challenge for both service providers and network operators. Traditional classification schemes exploiting port or payload analysis are becoming ineffective in actual networks, as many new applications are emerging. This paper presents the classification of HSDPA network traffic applications using Classification And Regression Tree (CART) and Support Vector Machine (SVM) with the session information as a basic measure. The session is bidirectional traffic stream between two hosts that is used as a basic measure and a unit of information. The authors acquired and processed HSDPA traffic from a real 3G network without sanitizing the data.
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