Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research

This chapter will appear in the Book “Malware: Handbook of Prevention and Detection”, published by Springer. The chapter explores how machine learning has been applied to build malware detection systems for the Windows operating system. The chapter starts by introducing the main components of a Machine Learning pipeline, highlighting the challenges of collecting and maintaining up-to-date datasets. Following this introduction, various state-of-the-art malware detectors are presented, encompassing both feature-based and deep learning-based detectors. Subsequent sections introduce the primary challenges encountered by machine learning-based malware detectors, including concept drift and adversarial attacks. Lastly, this chapter concludes by providing a brief overview of the ongoing research on adversarial defenses.

Recommended citation: Gibert, D. (2025). "Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research." In: Gritzalis, D., Choo, KK.R., Patsakis, C. (eds) Malware. Advances in Information Security, vol 91. Springer, Cham. https://doi.org/10.1007/978-3-031-66245-4_6 .
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