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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, In press, pp. 1-11, doi: 10.1109/TCC.2015.2481378.

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Title Hybrid consensus pruning of ensemble classifiers for big data malware detection
Author(s) Abawajy, Jemal
Chowdhury, MorshedORCID iD for Chowdhury, Morshed orcid.org/0000-0002-2866-4955
Kelarev, Andrei
Journal name IEEE transactions on cloud computing
Season In press
Start page 1
End page 11
Total pages 11
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-09-23
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

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