Adversarial training for tabular data with attack propagation. (arXiv:2307.15677v1 [cs.LG])

Adversarial attacks are a major concern in security-centered applications,
where malicious actors continuously try to mislead Machine Learning (ML) models
into wrongly classifying fraudulent activity as legitimate, whereas system
maintainers try to stop them. Adversarially training ML models that are robust
against such attacks can prevent business losses and reduce the work load of
system maintainers. In such applications data is often tabular and the space
available for attackers to manipulate undergoes complex feature engineering
transformations, to provide useful signals …

adversarial adversarial attacks applications attack attacks business data fraudulent losses machine machine learning maintainers major malicious malicious actors ml models security system training