Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue. (arXiv:2203.11410v1 [cs.CR])

Background: Machine learning techniques have been widely used and demonstrate
promising performance in many software security tasks such as software
vulnerability prediction. However, the class ratio within software
vulnerability datasets is often highly imbalanced (since the percentage of
observed vulnerability is usually very low). Goal: To help security
practitioners address software security data class imbalanced issues and
further help build better prediction models with resampled datasets. Method: We
introduce an approach called Dazzle which is an optimized version of
conditional …

address adversarial class data generative adversarial networks networks security