ALGorain, F.T. orcid.org/0000-0003-0547-1402 and Clark, J.A. orcid.org/0000-0002-9230-9739 (2023) Covering arrays ML HPO for static malware detection. Eng, 4 (1). pp. 543-554.
Abstract
Malware classification is a well-known problem in computer security. Hyper-parameter optimisation (HPO) using covering arrays (CAs) is a novel approach that can enhance machine learning classifier accuracy. The tuning of machine learning (ML) classifiers to increase classification accuracy is needed nowadays, especially with newly evolving malware. Four machine learning techniques were tuned using cAgen, a tool for generating covering arrays. The results show that cAgen is an efficient approach to achieve the optimal parameter choices for ML techniques. Moreover, the covering array shows a significant promise, especially cAgen with regard to the ML hyper-parameter optimisation community, malware detectors community and overall security testing. This research will aid in adding better classifiers for static PE malware detection.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | cAgen; combinatorial testing; covering arrays; machine learning; static PE malware detection; hyper-parameter optimisation; grid search |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Mar 2023 11:22 |
Last Modified: | 15 Mar 2023 11:22 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/eng4010032 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:197294 |