Toward Deep Learning Based Access Control

TL;DR

This paper explores the use of deep learning to improve access control systems, aiming to reduce manual model engineering and enhance adaptability in complex, dynamic environments.

Contribution

It proposes a novel deep learning-based access control framework, DLBAC, capable of replacing traditional models and addressing challenges in accuracy, generalization, and explainability.

Findings

DLBAC_alpha achieves promising accuracy on real-world datasets

The approach demonstrates good generalization to synthetic data

Explainability of the neural network model is discussed

Abstract

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.