Publication:
A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works

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dc.citedby2
dc.contributor.authorGorment N.Z.en_US
dc.contributor.authorSelamat A.en_US
dc.contributor.authorKrejcar O.en_US
dc.contributor.authorid57201987388en_US
dc.contributor.authorid24468984100en_US
dc.contributor.authorid14719632500en_US
dc.date.accessioned2023-05-29T09:10:44Z
dc.date.available2023-05-29T09:10:44Z
dc.date.issued2021
dc.descriptionBarium compounds; Cybersecurity; Data mining; Decision trees; Evolutionary algorithms; K-means clustering; Learning algorithms; Malware; Network security; Sodium compounds; Support vector machines; 'current; Comparatives studies; Cyber security; K-means; Machine learning algorithms; Malware attacks; Malware detection; Metaheuristic; Recent researches; Systematic literature review; Nearest neighbor searchen_US
dc.description.abstractEach year, malware issues remain one of the cybersecurity concerns since malware�s complexity is constantly changing as the innovation rapidly grows. As a result, malware attacks have affected everyday life from various mediums and ways. Therefore, a machine learning algorithm is one of the essential solutions in the security of computer systems to detect malware regarding the ability of machine learning algorithms to keep up with the evolution of malware. This paper is devoted to reviewing the most up-to-date research works from 2017 to 2021 on malware detection where machine learning algorithm including K-Means, Decision Tree, Meta-Heuristic, Na�ve Bayes, Neuro-fuzzy, Bayesian, Gaussian, Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and n-Grams was discovered using a systematic literature review. This paper aims at the following: (1) it describes each machine learning algorithm, (2) for each algorithm; it shows the performance of malware detection, and (3) we present the challenges and limitations of the algorithm during research processes. � 2021, Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-030-90235-3_41
dc.identifier.epage481
dc.identifier.scopus2-s2.0-85120527729
dc.identifier.spage469
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85120527729&doi=10.1007%2f978-3-030-90235-3_41&partnerID=40&md5=cff3750d65d41e9cbddebf036df426b1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26457
dc.identifier.volume13051 LNCS
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.titleA Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Worksen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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