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Many studies have proposed machine-learning (ML) models for malware detectionand classification, reporting an almost-perfect performance. However, theyassemble ground-truth in different ways, use diverse static- anddynamic-analysis techniques for feature extraction, and even differ on whatthey consider a malware family. As a consequence, our community still lacks anunderstanding of malware classification results: whether they are tied to thenature and distribution of the collected dataset, to what extent the number offamilies and samples in the training dataset influence performance, and howwell static and dynamic features complement each other.  This work sheds light on those open questions. by investigating the keyfactors influencing ML-based malware detection and classification. For this, wecollect the largest balanced malware dataset so far with 67K samples from 670families (100 samples each), and train state-of-the-art models for malwaredetection and family classification using our dataset. Our results reveal thatstatic features perform better than dynamic features, and that combining bothonly provides marginal improvement over static features. We discover nocorrelation between packing and classification accuracy, and that missingbehaviors in dynamically-extracted features highly penalize their performance.We also demonstrate how a larger number of families to classify make theclassification harder, while a higher number of samples per family increasesaccuracy. Finally, we find that models trained on a uniform distribution ofsamples per family better generalize on unseen data.

Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance