ABAC Rule Reduction via Similarity Computation

Hadj, Maryem Ait El; Benkaouz, Yahya; Freisleben, Bernd; Erradi, Mohammed

doi:10.1007/978-3-319-59647-1_7

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…Deep learning was used to identify relevant attributes [1] to mine ABAC policies from natural language. Other methods including classification trees [13], deep recurrent neural network (RNN) [53], K-Nearest Neighbor (KNN) [20], Decision Tree [8,9,71] and Restricted Boltzmann Machine (RBM) model [50] have also been used to mine ABAC policy. The first unsupervised learning-based ABAC mining method used k-modes clustering to mine rules from historical operation data [40].…”

Section: Related Workmentioning

confidence: 99%

Toward Deep Learning Based Access Control

Nobi,

Krishnan,

Huang

et al. 2022

Preprint

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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 α , 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. CCS CONCEPTS• Security and privacy → Access control; • Computing methodologies → Machine learning.

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