Abstract: Sophisticated cyberattacks are an increasing concern for individuals, businesses, and governments alike. Detecting malware remains a significant challenge, particularly due to the limitations of traditional methods in identifying new or unexpected threats. Machine Learning (ML) has emerged as a powerful solution, capable of analyzing large datasets, recognizing complex patterns, and adapting to rapidly changing attack strategies. This paper reviews the latest advancements in machine learning for malware analysis, shedding light on both its strengths and the challenges it faces. Additionally, it explores the current limitations of these approaches and outlines future research directions. Key recommendations include improving data preprocessing techniques to reduce information loss, utilizing distributed computing for greater efficiency, and maintaining balanced, up-to-date datasets to enhance model reliability. These strategies aim to improve the scalability, accuracy, and resilience of ML-driven malware detection systems.

Noura Alyemni and Mounir Frikha, “Exploring Machine Learning in Malware Analysis: Current Trends and Future Perspectives” International Journal of Advanced Computer Science and Applications(IJACSA), 16(1), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01601120

@article{Alyemni2025,
title = {Exploring Machine Learning in Malware Analysis: Current Trends and Future Perspectives},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01601120},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01601120},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {1},
author = {Noura Alyemni and Mounir Frikha}
}