Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research
It addresses the problem of malware detection for Windows users, but it is incremental as it synthesizes existing research without presenting new results.
This chapter reviews the application of machine learning to Windows malware detection, covering methods like feature-based and deep learning detectors, and discusses challenges such as concept drift and adversarial attacks.
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.