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dc.contributor.authorGyamfi, Nana Kwame,
dc.contributor.authorGoranin, Nikolaj,
dc.contributor.authorČeponis, Dainius,
dc.contributor.authorČenys, Antanas,
dc.date.accessioned2023-12-22T07:06:35Z
dc.date.available2023-12-22T07:06:35Z
dc.date.issued2023.
dc.identifier.urihttps://etalpykla.vilniustech.lt/xmlui/handle/123456789/153734
dc.description.abstractMalware poses a significant threat to computer systems and networks. This necessitates the development of effective detection mechanisms. Detection mechanisms dependent on signatures for attack detection perform poorly due to high false negatives. This limitation is attributed to the inability to detect zero-day attacks, polymorphic malware, increasing signature base, and detection speed. To achieve rapid detection, automated system-level malware detection using machine learning approaches, leveraging the power of artificial intelligence to identify and mitigate malware attacks, has emerged as a promising solution. This comprehensive review aims to provides a detailed analysis of the status quo in malware detection by exploring the fundamentals of machine learning techniques for malware detection. The review is largely based on the PRISMA approach for article search methods and selection from four databases. Keywords were identified together with inclusion and exclusion criteria. The review seeks feature extraction and selection methods that enhance the accuracy and precision of detection algorithms. Evaluation metrics and common datasets were used to assess the performance of the system-level malware detection techniques. A comparative analysis of different machine learning approaches, emphasizing their strengths, weaknesses, and performance in detecting system-level malware is presented together with the limitations of the detection techniques. The paper concludes with future research opportunities, particularly in applying artificial intelligence, and provides a resource for researchers and cybersecurity professionals seeking to understand and advance automated system-level malware detection using machine learning.eng
dc.formatPDF
dc.format.extentp. 1-23.
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyDOAJ
dc.relation.isreferencedbyINSPEC
dc.rightsLaisvai prieinamas internete.
dc.source.urihttps://www.mdpi.com/2076-3417/13/21/11908
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:180545547/datastreams/MAIN/content
dc.titleAutomated system-level malware detection using machine learning: A comprehensive review /
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsThis 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/).
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references45
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyFundamentinių mokslų fakultetas / Faculty of Fundamental Sciences
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.studydirectionB04 - Informatikos inžinerija / Informatics engineering
dc.subject.studydirectionB03 - Programų sistemos / Software engineering
dc.subject.studydirectionB01 - Informatika / Informatics
dc.subject.vgtuprioritizedfieldsIK0101 - Informacijos ir informacinių technologijų sauga / Information and Information Technologies Security
dc.subject.ltspecializationsL106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies
dc.subject.enanomaly detection
dc.subject.enautomated system classification
dc.subject.encybersecurity machine learning
dc.subject.enmalware detection
dcterms.sourcetitleApplied sciences: Special issue: Security challenges for the internet of things and mobile networks.
dc.description.issueiss. 21
dc.description.volumevol. 13
dc.publisher.nameMDPI
dc.publisher.cityBasel
dc.identifier.doi001100279900001
dc.identifier.doi10.3390/app132111908
dc.identifier.elaba180545547


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