Machine learning algorithm for malware detection: taxonomy, current challenges, and future directions.

• Main Authors: Gorment, Nor Zakiah, Selamat, Ali, Cheng, Lim Kok, Krejcar, Ondrej
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.utm.my/104914/1/NorZakiahGorment2023_MachineLearningAlgorithmforMalwareDetectionTaxonomy.pdf; http://eprints.utm.my/104914/; http://dx.doi.org/10.1109/ACCESS.2023.3256979
• Institution: Universiti Teknologi Malaysia

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.