Hybrid consensus pruning of ensemble classifiers for big data malware detection Abawajy, Jemal, Chowdhury, Morshed and Kelarev, Andrei 2015, Hybrid consensus pruning of ensemble classifiers for big data malware detection, IEEE transactions on cloud computing, vol. 3, no. 2, pp. 1-11, doi: 10.1109/TCC.2015.2481378. Attached Files Name Description MIMEType Size Downloads Title Hybrid consensus pruning of ensemble classifiers for big data malware detection Author(s) Abawajy, Jemal Chowdhury, Morshed orcid.org/0000-0002-2866-4955 Kelarev, Andrei Journal name IEEE transactions on cloud computing Volume number 3 Issue number 2 Start page 1 End page 11 Total pages 11 Publisher IEEE Place of publication Piscataway, N.J. Publication date 2015-10 ISSN 2168-7161 Keyword(s) ensemble pruning ensemble classifiers malware big data Summary One of the major challenges for safeguarding the security of big data in the cloud is how to detect and prevent malicious software (malware). Despite of the fact that security and privacy are critical issues in big data, more research needs to be done in this area. As malware can affect the reliability of the data and subsequently the reputation of the system, it is critical to detect and remove malware from a system as early as possible. Recently, ensembles that combine a set of classifiers have been proposed as an efficient approach for malware detection. Unfortunately, the size, memory and processing requirements as well as the high cost of data transfer during training and operation make large ensemble classifiers unsuitable for big data in the cloud. To address this problem, we propose a new advanced ensemble pruning method, Hybrid Consensus Pruning (HCP), which is the first pruning algorithm that employs a fast consensus function to combine several classifier classes into one scheme. To test the effectiveness of the HCP method, we conducted experiments comparing its performance with Ensemble Pruning via Individual Contribution ordering (EPIC), Directed Hill Climbing Ensemble Pruning (DHCEP) and K-Means Pruning approaches for pruning very large ensemble classifiers for malware detection. The results of the experiments show that HCP achieved better results by producing better ensemble classifiers as compared to those created by EPIC, DHCEP and K-Means Pruning. Language eng DOI 10.1109/TCC.2015.2481378 Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1.1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2015, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30088692 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   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 Thu, 10 Nov 2016, 13:10:30 EST Fri, 11 Nov 2016, 04:02:19 EST Fri, 11 Nov 2016, 07:24:33 EST Wed, 10 May 2017, 06:03:26 EST Wed, 14 Jun 2017, 09:41:02 EST Wed, 14 Jun 2017, 09:44:52 EST Wed, 14 Jun 2017, 09:45:39 EST Thu, 07 Sep 2017, 15:38:54 EST Thu, 07 Sep 2017, 15:40:07 EST Fri, 08 Sep 2017, 08:27:48 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 29 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Thu, 10 Nov 2016, 13:10:30 EST

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