FDM: A Framework for Decision-Making to Build ML-Based Malware Detection Systems

Selecting appropriate machine learning ML configurations for malware detection is a complex, multi-criteria problem. Model choice, feature engineering, and update mechanisms must jointly satisfy operational constraints that vary across deployment contexts. This paper proposes the Framework for...