AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs. (arXiv:2212.13989v1 [cs.CR])

Machine Learning-as-a-Service systems (MLaaS) have been largely developed for
cybersecurity-critical applications, such as detecting network intrusions and
fake news campaigns. Despite effectiveness, their robustness against
adversarial attacks is one of the key trust concerns for MLaaS deployment. We
are thus motivated to assess the adversarial robustness of the Machine Learning
models residing at the core of these security-critical applications with
categorical inputs. Previous research efforts on accessing model robustness
against manipulation of categorical inputs are specific to use cases and …

applications assessment critical cybersecurity domain inputs robustness