Taming Data Challenges in ML-based Security Tasks Using Generative AI

arXiv:2507.06092v4 Announce Type: replace-cross Abstract: Machine learning-based supervised classifiers are widely used for security tasks, and their improvement has been largely focused on algorithmic advancements. We argue that data challenges that negatively impact the performance of these classifiers have received limited attention. We address the following research question: Can developments in Generative AI (GenAI) address these data challenges and improve classifier performance? We propose augmenting training datasets with synthetic data generated using GenAI techniques to improve class

Source: arXiv cs.LG — read the full report at the original publisher.

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