On addressing the imbalance problem : A correlated KNN approach for network traffic classification

Wu,D, Chen,X, Chen,C, Zhang,J, Xiang,Y and Zhou,W 2014, On addressing the imbalance problem : A correlated KNN approach for network traffic classification. In Au,MH, Carminati,B and Kuo,CCJ (ed), Network and System Security, Springer International Publishing, Heidelberg, Germany, pp.138-151, doi: 10.1007/978-3-319-11698-3.

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Title On addressing the imbalance problem : A correlated KNN approach for network traffic classification
Author(s) Wu,D
Chen,X
Chen,C
Zhang,JORCID iD for Zhang,J orcid.org/0000-0002-2189-7801
Xiang,YORCID iD for Xiang,Y orcid.org/0000-0001-5252-0831
Zhou,WORCID iD for Zhou,W orcid.org/0000-0002-1680-2521
Title of book Network and System Security
Editor(s) Au,MH
Carminati,B
Kuo,CCJ
Publication date 2014
Series Lecture Notes in Computer Science
Chapter number 11
Total chapters 46
Start page 138
End page 151
Total pages 14
Publisher Springer International Publishing
Place of Publication Heidelberg, Germany
Keyword(s) Science & Technology
Technology
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
Summary With the arrival of big data era, the Internet traffic is growing exponentially. A wide variety of applications arise on the Internet and traffic classification is introduced to help people manage the massive applications on the Internet for security monitoring and quality of service purposes. A large number of Machine Learning (ML) algorithms are introduced to deal with traffic classification. A significant challenge to the classification performance comes from imbalanced distribution of data in traffic classification system. In this paper, we proposed an Optimised Distance-based Nearest Neighbor (ODNN), which has the capability of improving the classification performance of imbalanced traffic data. We analyzed the proposed ODNN approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments were implemented on the real-world traffic dataset. The results show that the performance of “small classes” can be improved significantly even only with small number of training data and the performance of “large classes” remains stable.
ISBN 9783319116983
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-11698-3
Field of Research 080504 Ubiquitous Computing
080609 Information Systems Management
Socio Economic Objective 890101 F
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2014, Springer International Publishing
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071874

Document type: Book Chapter
Collections: School of Information Technology
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