Detection and classification of anomaly intrusion using hierarchy clustering and SVM Tang, Chenghua, Xiang, Yang, Wang, Yu, Qian, Junyan and Qiang, Baohua 2016, Detection and classification of anomaly intrusion using hierarchy clustering and SVM, Security and communication networks, vol. 9, no. 16, pp. 3401-3411, doi: 10.1002/sec.1547. Attached Files Name Description MIMEType Size Downloads Title Detection and classification of anomaly intrusion using hierarchy clustering and SVM Author(s) Tang, Chenghua Xiang, Yang orcid.org/0000-0001-5252-0831 Wang, Yu orcid.org/0000-0002-9807-2293 Qian, Junyan Qiang, Baohua Journal name Security and communication networks Volume number 9 Issue number 16 Start page 3401 End page 3411 Total pages 11 Publisher John Wiley & Sons Place of publication Hoboken, N.J. Publication date 2016-11-10 ISSN 1939-0114 1939-0122 Keyword(s) anomaly intrusion detection fuzzy c-means clustering membership function support vector machine Science & Technology Technology Computer Science, Information Systems Telecommunications Computer Science COMPUTER-NETWORKS SYSTEMS Summary Anomaly detection as a kind of intrusion detection is good at detecting the unknown attacks or new attacks, and it has attracted much attention during recent years. In this paper, a new hierarchy anomaly intrusion detection model that combines the fuzzy c-means (FCM) based on genetic algorithm and SVM is proposed. During the process of detecting intrusion, the membership function and the fuzzy interval are applied to it, and the process is extended to soft classification from the previous hard classification. Then a fuzzy error correction sub interval is introduced, so when the detection result of a data instance belongs to this range, the data will be re-detected in order to improve the effectiveness of intrusion detection. Experimental results show that the proposed model can effectively detect the vast majority of network attack types, which provides a feasible solution for solving the problems of false alarm rate and detection rate in anomaly intrusion detection model. Language eng DOI 10.1002/sec.1547 Field of Research 080303 Computer System Security Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2016, John Wiley & Sons Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30085317 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)   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. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Wed, 10 Aug 2016, 05:00:37 EST Thu, 18 Aug 2016, 13:29:02 EST Thu, 18 Aug 2016, 13:36:22 EST Thu, 18 Aug 2016, 13:45:55 EST Mon, 22 Aug 2016, 10:26:02 EST Tue, 23 Aug 2016, 06:09:51 EST Fri, 07 Oct 2016, 10:54:27 EST Fri, 07 Oct 2016, 11:03:09 EST Mon, 10 Oct 2016, 14:58:17 EST Wed, 11 Jan 2017, 09:54:39 EST Mon, 22 May 2017, 10:00:21 EST Mon, 22 May 2017, 10:02:31 EST Fri, 29 Nov 2019, 16:35:51 EST Filtered Full Citation counts:   Cited 4 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 322 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Mon, 22 Aug 2016, 10:26:02 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.