Internet traffic clustering with constraints

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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Title Internet traffic clustering with constraints
Author(s) Wang, Yu
Xiang, YangORCID iD for Xiang, Yang
Zhang, JunORCID iD for Zhang, Jun
Yu, Shunzheng
Conference name IEEE Wireless Communications and Mobile Computing. Conference (8th : 2012 : Limassol, Cyprus)
Conference location Limassol, Cyprus
Conference dates 27-31 Aug. 2012
Title of proceedings IWCMC 2012 : Proceedings of the IEEE 8th Wireless Communications and Mobile Computing Conference
Editor(s) [Unknown]
Publication date 2012
Conference series IEEE Wireless Communications and Mobile Computing. Conference
Start page 619
End page 624
Total pages 6
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) constrained clustering
machine learning
semi-supervised learning
traffic classification
Summary 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.
ISBN 9781457713781
Language eng
Field of Research 080503 Networking and Communications
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890199 Communication Networks and Services not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2012, IEEE
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