VAEs generate synthetic malware to augment datasets, yielding reported gains in accuracy, precision, recall, and F1 for three ML classifiers.
Effective and efficient hybrid Android malware classification using pseudo-label stacked auto-encoder,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Enhancing Malware Detection with Generative AI: Using Variational Autoencoders to Boost Machine Learning Classifiers' Performance
VAEs generate synthetic malware to augment datasets, yielding reported gains in accuracy, precision, recall, and F1 for three ML classifiers.