Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research
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Abstract (arXiv)
In this chapter, readers will explore how machine learning has been applied to build malware detection systems designed for the Windows operating system. This 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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 26 of its 53 references that are in the map. First posted on arXiv 29 Apr 2024.
Builds on
26 of 53 references.
References · 26
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