# Toward Deep Learning Based Access Control

arXiv: https://arxiv.org/abs/2203.15124 · HTML: https://mlsanity.com/paper/2203.15124/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [Distributionally Adversarial Attack](https://mlsanity.com/paper/1808.05537/) (2018)
- [Mining Attribute-Based Access Control Policies from Logs](https://mlsanity.com/paper/1403.5715/) (2014)
- [Efficient and Extensible Policy Mining for Relationship-Based Access Control](https://mlsanity.com/paper/1903.07530/) (2019)
- [A Decision Tree Learning Approach for Mining Relationship-Based Access Control Policies](https://mlsanity.com/paper/1909.12095/) (2019)
- [The Next 700 Policy Miners: A Universal Method for Building Policy Miners](https://mlsanity.com/paper/1908.05994/) (2019)

## Built on by (most cited first, 4 of 4)

- [Can LLMs Make (Personalized) Access Control Decisions?](https://mlsanity.com/paper/2511.20284/) (2025)
- [Machine Learning in Access Control: A Taxonomy and Survey](https://mlsanity.com/paper/2207.01739/) (2022)
- [Towards Harnessing the Power of LLMs for ABAC Policy Mining](https://mlsanity.com/paper/2511.18098/) (2025)
- [Agentic-ZTA: A Multi-Agent Architecture for Autonomous Zero Trust Enforcement](https://mlsanity.com/paper/2610.05782/) (2026)

## Abstract (arXiv)

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.