A system call refinement-based enhanced Minimum Redundancy Maximum Relevance method for ransomware early detection Ahmed, Yahye Abukar, Koçer, Barıs, Huda, Shamsul, Saleh Al-rimy, Bander Ali and Hassan, Mohammad Mehedi 2020, A system call refinement-based enhanced Minimum Redundancy Maximum Relevance method for ransomware early detection, Journal of network and computer applications, vol. 167, pp. 1-14, doi: 10.1016/j.jnca.2020.102753. Attached Files Name Description MIMEType Size Downloads Title A system call refinement-based enhanced Minimum Redundancy Maximum Relevance method for ransomware early detection Author(s) Ahmed, Yahye Abukar Koçer, Barıs Huda, Shamsul orcid.org/0000-0001-7848-0508 Saleh Al-rimy, Bander Ali Hassan, Mohammad Mehedi Journal name Journal of network and computer applications Volume number 167 Article ID 102753 Start page 1 End page 14 Total pages 14 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2020-10 ISSN 1084-8045 Keyword(s) System call Ransomware Maximum relevance and minimum redundancy N-Grams Refinement Language eng DOI 10.1016/j.jnca.2020.102753 Indigenous content off Field of Research 0899 Other Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal Persistent URL http://hdl.handle.net/10536/DRO/DU:30140322 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 Tue, 21 Jul 2020, 22:41:21 EST Wed, 22 Jul 2020, 05:01:26 EST Wed, 22 Jul 2020, 08:06:23 EST Thu, 23 Jul 2020, 06:03:44 EST Sat, 25 Jul 2020, 02:34:50 EST Fri, 14 Aug 2020, 09:23:23 EST Fri, 14 Aug 2020, 09:25:44 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 17 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Tue, 21 Jul 2020, 22:41:21 EST

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