Publication:
Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

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dc.citedby18
dc.contributor.authorGorment N.Z.en_US
dc.contributor.authorSelamat A.en_US
dc.contributor.authorCheng L.K.en_US
dc.contributor.authorKrejcar O.en_US
dc.contributor.authorid57201987388en_US
dc.contributor.authorid24468984100en_US
dc.contributor.authorid57188850203en_US
dc.contributor.authorid14719632500en_US
dc.date.accessioned2024-10-14T03:21:22Z
dc.date.available2024-10-14T03:21:22Z
dc.date.issued2023
dc.description.abstractMalware has emerged as a cyber security threat that continuously changes to target computer systems, smart devices, and extensive networks with the development of information technologies. As a result, malware detection has always been a major worry and a difficult issue, owing to shortcomings in performance accuracy, analysis type, and malware detection approaches that fail to identify unexpected malware attacks. This paper seeks to conduct a thorough systematic literature review (SLR) and offer a taxonomy of machine learning methods for malware detection that considers these problems by analyzing 77 chosen research works related to malware detection using machine learning algorithm. The research investigates malware and machine learning in the context of cybersecurity, including malware detection taxonomy and machine learning algorithm classification into numerous categories. Furthermore, the taxonomy was used to evaluate the most recent machine learning algorithm and analysis. The paper also examines the obstacles and associated concerns encountered in malware detection and potential remedies. Finally, to address the related issues that would motivate researchers in their future work, an empirical study was utilized to assess the performance of several machine learning algorithms. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ACCESS.2023.3256979
dc.identifier.epage141089
dc.identifier.scopus2-s2.0-85151320871
dc.identifier.spage141045
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85151320871&doi=10.1109%2fACCESS.2023.3256979&partnerID=40&md5=cc7fd8c7850499ec01197590368a9369
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34644
dc.identifier.volume11
dc.pagecount44
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.subjectmachine learning algorithms
dc.subjectMalware detection
dc.subjectstate-of-the-art
dc.subjectArtificial intelligence
dc.subjectCybersecurity
dc.subjectIntrusion detection
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectMalware
dc.subjectNetwork security
dc.subjectTaxonomies
dc.subjectTrees (mathematics)
dc.subject'current
dc.subjectClassification-tree analysis
dc.subjectCyber security
dc.subjectMachine learning algorithms
dc.subjectMachine-learning
dc.subjectMalware detection
dc.subjectMalwares
dc.subjectPerformance
dc.subjectState of the art
dc.subjectSupport vectors machine
dc.subjectData mining
dc.titleMachine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directionsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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