Semi-supervised and compound classification of network traffic

Zhang, Jun, Chen, Chao, Xiang, Yang and Zhou, Wanlei 2012, Semi-supervised and compound classification of network traffic, in ICDCSW 2012 : Proceedings of the 32nd IEEE International Conference on Distributed Computing Systems Workshops, IEEE Computer Society, Piscataway, N. J., pp. 617-621.

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Title Semi-supervised and compound classification of network traffic
Author(s) Zhang, Jun
Chen, Chao
Xiang, Yang
Zhou, Wanlei
Conference name IEEE International Conference on Distributed Computing Systems. Workshop (32nd : 2012 : Macau, China)
Conference location Macau, China
Conference dates 18 -21 Jun. 2012
Title of proceedings ICDCSW 2012 : Proceedings of the 32nd IEEE International Conference on Distributed Computing Systems Workshops
Editor(s) [Unknown]
Publication date 2012
Conference series IEEE International Conference on Distributed Computing Systems. Workshop
Start page 617
End page 621
Total pages 5
Publisher IEEE Computer Society
Place of publication Piscataway, N. J.
Keyword(s) compound classifier
semi-supervised
traffic classification
Summary This paper presents a new semi-supervised method to effectively improve traffic classification performance when few supervised training data are available. Existing semi supervised methods label a large proportion of testing flows as unknown flows due to limited supervised information, which severely affects the classification performance. To address this problem, we propose to incorporate flow correlation into both training and testing stages. At the training stage, we make use of flow correlation to extend the supervised data set by automatically labeling unlabeled flows according to their correlation to the pre-labeled flows. Consequently, the traffic classifier has better performance due to the extended size and quality of the supervised data sets. At the testing stage, the correlated flows are identified and classified jointly by combining their individual predictions, so as to further boost the classification accuracy. The empirical study on the real-world network traffic shows that the proposed method outperforms the state-of-the-art flow statistical feature based classification methods.
ISBN 9781467314237
ISSN 1545-0678
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
Persistent URL http://hdl.handle.net/10536/DRO/DU:30049565

Document type: Conference Paper
Collection: School of Information Technology
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