Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance. (arXiv:2307.14657v1 [cs.CR])

Many studies have proposed machine-learning (ML) models for malware detection
and classification, reporting an almost-perfect performance. However, they
assemble ground-truth in different ways, use diverse static- and
dynamic-analysis techniques for feature extraction, and even differ on what
they consider a malware family. As a consequence, our community still lacks an
understanding of malware classification results: whether they are tied to the
nature and distribution of the collected dataset, to what extent the number of
families and samples in the training …

analysis classification datasets decoding deep dive detection dive dynamic feature machine machine learning malware malware classification malware detection perfect performance reporting secrets studies techniques truth