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. Attached Files Name Description MIMEType Size Downloads Title On addressing the imbalance problem : A correlated KNN approach for network traffic classification Author(s) Wu,D Chen,X Chen,C Zhang,J orcid.org/0000-0002-2189-7801 Xiang,Y orcid.org/0000-0001-5252-0831 Zhou,W 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: Faculty of Science, Engineering and Built Environment School of Information Technology ERA Postprints 2018 ERA Submission Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 30 Mar 2015, 10:21:20 EST Fri, 17 Apr 2015, 16:11:33 EST Wed, 22 Apr 2015, 15:14:26 EST Wed, 22 Apr 2015, 15:38:15 EST Wed, 22 Apr 2015, 15:49:59 EST Thu, 23 Apr 2015, 11:03:15 EST Thu, 23 Apr 2015, 11:05:46 EST Mon, 11 May 2015, 16:06:29 EST Fri, 06 Apr 2018, 09:43:11 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 594 Abstract Views, 19 File Downloads  -  Detailed Statistics Created: Wed, 22 Apr 2015, 15:14:26 EST

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