Covering Arrays ML HPO for Static Malware Detection

Autor:	Fahad T. ALGorain, John A. Clark
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	cAgen combinatorial testing covering arrays machine learning static PE malware detection hyper-parameter optimisation Electrical engineering. Electronics. Nuclear engineering TK1-9971
Zdroj:	Eng, Vol 4, Iss 1, Pp 543-554 (2023)
Druh dokumentu:	article
ISSN:	2673-4117
DOI:	10.3390/eng4010032
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/afa987690af64b2cbd15c7acee074ce2 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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