A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works
Barium 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 att...
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Springer Science and Business Media Deutschland GmbH
2023
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my.uniten.dspace-264572023-05-29T17:10:44Z A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works Gorment N.Z. Selamat A. Krejcar O. 57201987388 24468984100 14719632500 Barium 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 search Each 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. Final 2023-05-29T09:10:44Z 2023-05-29T09:10:44Z 2021 Conference Paper 10.1007/978-3-030-90235-3_41 2-s2.0-85120527729 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120527729&doi=10.1007%2f978-3-030-90235-3_41&partnerID=40&md5=cff3750d65d41e9cbddebf036df426b1 https://irepository.uniten.edu.my/handle/123456789/26457 13051 LNCS 469 481 Springer Science and Business Media Deutschland GmbH Scopus |
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Barium 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 search |
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57201987388 |
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57201987388 Gorment N.Z. Selamat A. Krejcar O. |
| format |
Conference Paper |
| author |
Gorment N.Z. Selamat A. Krejcar O. |
| spellingShingle |
Gorment N.Z. Selamat A. Krejcar O. A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works |
| author_sort |
Gorment N.Z. |
| title |
A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works |
| title_short |
A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works |
| title_full |
A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works |
| title_fullStr |
A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works |
| title_full_unstemmed |
A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works |
| title_sort |
recent research on malware detection using machine learning algorithm: current challenges and future works |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2023 |
| _version_ |
1806426633824894976 |