Wang, Yu, Xiang, Yang, Zhang, Jun and Yu, Shunzheng 2012, Internet traffic clustering with constraints, in IWCMC 2012 : Proceedings of the IEEE 8th Wireless Communications and Mobile Computing Conference, IEEE, Piscataway, N. J., pp. 619-624.
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Due to the limitations of the traditional port-based and payload-based traffic classification approaches, the past decade has seen extensive work on utilizing machine learning techniques to classify network traffic based on packet and flow level features. In particular, previous studies have shown that the unsupervised clustering approach is both accurate and capable of discovering previously unknown application classes. In this paper, we explore the utility of side information in the process of traffic clustering. Specifically, we focus on the flow correlation information that can be efficiently extracted from packet headers and expressed as instance-level constraints, which indicate that particular sets of flows are using the same application and thus should be put into the same cluster. To incorporate the constraints, we propose a modified constrained K-Means algorithm. A variety of real-world traffic traces are used to show that the constraints are widely available. The experimental results indicate that the constrained approach not only improves the quality of the resulted clusters, but also speeds up the convergence of the clustering process.
Field of Research
080503 Networking and Communications 080109 Pattern Recognition and Data Mining
Socio Economic Objective
890199 Communication Networks and Services not elsewhere classified