Toward Deep Learning Based Access Control

• URL: http://arxiv.org/abs/2203.15124v1
• Date: Mon, 28 Mar 2022 22:05:11 GMT
• Title: Toward Deep Learning Based Access Control
• Authors: Mohammad Nur Nobi, Ram Krishnan, Yufei Huang, Mehrnoosh Shakarami, Ravi Sandhu
• Abstract summary: This paper proposes Deep Learning Based Access Control (DLBAC) by leveraging significant advances in deep learning technology. DLBAC could complement and, in the long-term, has the potential to even replace, classical access control models with a neural network. We demonstrate the feasibility of the proposed approach by addressing issues related to accuracy, generalization, and explainability.
• Score: 3.2511618464944547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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