Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions
Malware 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 pe...
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my.uniten.dspace-346442024-10-14T11:21:22Z Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions Gorment N.Z. Selamat A. Cheng L.K. Krejcar O. 57201987388 24468984100 57188850203 14719632500 machine learning algorithms Malware detection state-of-the-art Artificial intelligence Cybersecurity Intrusion detection Learning algorithms Learning systems Malware Network security Taxonomies Trees (mathematics) 'current Classification-tree analysis Cyber security Machine learning algorithms Machine-learning Malware detection Malwares Performance State of the art Support vectors machine Data mining Malware 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. Final 2024-10-14T03:21:22Z 2024-10-14T03:21:22Z 2023 Article 10.1109/ACCESS.2023.3256979 2-s2.0-85151320871 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151320871&doi=10.1109%2fACCESS.2023.3256979&partnerID=40&md5=cc7fd8c7850499ec01197590368a9369 https://irepository.uniten.edu.my/handle/123456789/34644 11 141045 141089 All Open Access Gold Open Access Institute of Electrical and Electronics Engineers Inc. Scopus |
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machine learning algorithms Malware detection state-of-the-art Artificial intelligence Cybersecurity Intrusion detection Learning algorithms Learning systems Malware Network security Taxonomies Trees (mathematics) 'current Classification-tree analysis Cyber security Machine learning algorithms Machine-learning Malware detection Malwares Performance State of the art Support vectors machine Data mining |
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machine learning algorithms Malware detection state-of-the-art Artificial intelligence Cybersecurity Intrusion detection Learning algorithms Learning systems Malware Network security Taxonomies Trees (mathematics) 'current Classification-tree analysis Cyber security Machine learning algorithms Machine-learning Malware detection Malwares Performance State of the art Support vectors machine Data mining Gorment N.Z. Selamat A. Cheng L.K. Krejcar O. Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions |
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Malware 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. |
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57201987388 |
| author_facet |
57201987388 Gorment N.Z. Selamat A. Cheng L.K. Krejcar O. |
| format |
Article |
| author |
Gorment N.Z. Selamat A. Cheng L.K. Krejcar O. |
| author_sort |
Gorment N.Z. |
| title |
Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions |
| title_short |
Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions |
| title_full |
Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions |
| title_fullStr |
Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions |
| title_full_unstemmed |
Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions |
| title_sort |
machine learning algorithm for malware detection: taxonomy, current challenges, and future directions |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2024 |
| _version_ |
1814060108249825280 |
| score |
13.222552 |