Classification of correlated internet traffic flows Zhang, Jun, Chen, Chao, Xiang, Yang and Zhou, Wanlei 2012, Classification of correlated internet traffic flows, in TrustCom 2012 : Proceedings of the 11th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, IEEE, Piscataway, N. J., pp. 490-496. Attached Files Name Description MIMEType Size Downloads Title Classification of correlated internet traffic flows Author(s) Zhang, Jun orcid.org/0000-0002-2189-7801 Chen, Chao Xiang, Yang orcid.org/0000-0001-5252-0831 Zhou, Wanlei Conference name IEEE International Conference on Trust, Security and Privacy in Computing and Communications (11th : 2012 : Liverpool, England) Conference location Liverpool, England Conference dates 25-27 Jun. 2012 Title of proceedings TrustCom 2012 : Proceedings of the 11th IEEE International Conference on Trust, Security and Privacy in Computing and Communications Editor(s) Min, Geyong Wu, Yulei Lei, Liu (Chris) Jin, Xiaolong Jarvis, Stephen Al-Dubai, Ahmed Y. Publication date 2012 Conference series IEEE International Conference on Trust, Security and Privacy in Computing and Communications Start page 490 End page 496 Total pages 7 Publisher IEEE Place of publication Piscataway, N. J. Keyword(s) traffic classification network security naive Bayes Summary A critical problem for Internet traffic classification is how to obtain a high-performance statistical feature based classifier using a small set of training data. The solutions to this problem are essential to deal with the encrypted applications and the new emerging applications. In this paper, we propose a new Naive Bayes (NB) based classification scheme to tackle this problem, which utilizes two recent research findings, feature discretization and flow correlation. A new bag-of-flow (BoF) model is firstly introduced to describe the correlated flows and it leads to a new BoF-based traffic classification problem. We cast the BoF-based traffic classification as a specific classifier combination problem and theoretically analyze the classification benefit from flow aggregation. A number of combination methods are also formulated and used to aggregate the NB predictions of the correlated flows. Finally, we carry out a number of experiments on a large scale real-world network dataset. The experimental results show that the proposed scheme can achieve significantly higher classification accuracy and much faster classification speed with comparison to the state-of-the-art traffic classification methods. ISBN 0769547451 9780769547459 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:30049566 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology 2018 ERA Submission Related Links Link Description Connect to published version (restricted access) 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 Thu, 29 Nov 2012, 07:59:38 EST Mon, 10 Dec 2012, 08:11:12 EST Mon, 10 Dec 2012, 08:13:11 EST Mon, 20 May 2013, 15:19:12 EST Tue, 21 May 2013, 08:23:21 EST Wed, 27 Nov 2013, 15:03:55 EST Fri, 08 Aug 2014, 10:23:33 EST Wed, 27 Aug 2014, 23:13:19 EST Fri, 17 Oct 2014, 11:51:54 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 8 times in Scopus Search Google Scholar Access Statistics: 644 Abstract Views, 12 File Downloads  -  Detailed Statistics Created: Thu, 29 Nov 2012, 07:59:38 EST