A fast malware feature selection approach using a hybrid of multi-linear and stepwise binary logistic regression Huda, Shamsul, Abawajy, Jemal, Abdollahian, Mali, Islam, Rafiqul and Yearwood, John Leighton 2017, A fast malware feature selection approach using a hybrid of multi-linear and stepwise binary logistic regression, Concurrency computation, vol. 29, no. 23, Special Issue, pp. 1-18, doi: 10.1002/cpe.3912. Attached Files Name Description MIMEType Size Downloads Title A fast malware feature selection approach using a hybrid of multi-linear and stepwise binary logistic regression Author(s) Huda, Shamsul orcid.org/0000-0001-7848-0508 Abawajy, Jemal orcid.org/0000-0001-8962-1222 Abdollahian, Mali Islam, Rafiqul Yearwood, John Leighton orcid.org/0000-0002-7562-6767 Journal name Concurrency computation Volume number 29 Issue number 23 Season Special Issue Start page 1 End page 18 Total pages 18 Publisher Wiley Place of publication Chichester, Eng. Publication date 2017-12 ISSN 1532-0626 1532-0634 Keyword(s) malware detection binary logistic regression stepwise regression API call statistics AIC criteria chi-square Notes Special Issue: Combined Special issues on Applications and techniques in information and network security (CSTA2015) and International conference on innovative network systems and applications held under the federated conference on computer science and information systems (FedCSis‐INetSApp2015) Language eng DOI 10.1002/cpe.3912 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, Wiley Persistent URL http://hdl.handle.net/10536/DRO/DU:30089582 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology 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, 28 Nov 2016, 16:00:33 EST Tue, 29 Nov 2016, 04:04:22 EST Tue, 29 Nov 2016, 05:06:35 EST Wed, 30 Nov 2016, 12:13:07 EST Thu, 08 Dec 2016, 11:35:37 EST Fri, 21 Apr 2017, 13:47:47 EST Mon, 22 May 2017, 12:30:53 EST Fri, 15 Sep 2017, 13:36:31 EST Mon, 18 Sep 2017, 08:18:17 EST Sat, 25 Nov 2017, 01:45:48 EST Sat, 02 Dec 2017, 01:31:45 EST Fri, 20 Jul 2018, 10:18:02 EST Filtered Full Citation counts:   Cited 3 times in TR Web of Science Cited 6 times in Scopus Search Google Scholar Access Statistics: 413 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 28 Nov 2016, 16:00:33 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.