  A common trait of current access control approaches is the challenging need
to engineer abstract and intuitive access control models. This entails
designing access control information in the form of roles (RBAC), attributes
(ABAC), or relationships (ReBAC) as the case may be, and subsequently,
designing access control rules. This framework has its benefits but has
significant limitations in the context of modern systems that are dynamic,
complex, and large-scale, due to which it is difficult to maintain an accurate
access control state in the system for a human administrator. This paper
proposes Deep Learning Based Access Control (DLBAC) by leveraging significant
advances in deep learning technology as a potential solution to this problem.
We envision that DLBAC could complement and, in the long-term, has the
potential to even replace, classical access control models with a neural
network that reduces the burden of access control model engineering and
updates. Without loss of generality, we conduct a thorough investigation of a
candidate DLBAC model, called DLBAC_alpha, using both real-world and synthetic
datasets. We demonstrate the feasibility of the proposed approach by addressing
issues related to accuracy, generalization, and explainability. We also discuss
challenges and future research directions.
