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.

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Title A fast malware feature selection approach using a hybrid of multi-linear and stepwise binary logistic regression
Author(s) Huda, ShamsulORCID iD for Huda, Shamsul orcid.org/0000-0001-7848-0508
Abawajy, JemalORCID iD for Abawajy, Jemal orcid.org/0000-0001-8962-1222
Abdollahian, Mali
Islam, Rafiqul
Yearwood, John LeightonORCID iD for 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

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