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Role-based access control (RBAC) is one of the most widely authorization models used by organizations. In RBAC, accesses are controlled based on the roles of users within the organization. The flexibility and usability of RBAC have encouraged organizations to migrate from traditional discretionary access control (DAC) models to RBAC. The most challenging step in this migration is role mining, which is the process of extracting meaningful roles from existing access control lists. Although various approaches have been proposed to address this NP-complete role mining problem in the literature, they either suffer from low scalability, or present heuristics that suffer from low accuracy. In this paper, we propose an accurate and scalable approach to the role mining problem. To this aim, we represent user-permission assignments as a bipartite graph where nodes are users and permissions, and edges are user-permission assignments. Next, we introduce an efficient deep learning algorithm based on random walk sampling to learn low-dimensional representations of the graph, such that permissions that are assigned to similar users are closer in this new space. Then, we use k-means and GMM clustering techniques to cluster permission nodes into roles. We show the effectiveness of our proposed approach by testing it on different datasets. Experimental results show that our approach performs accurate role mining, even for large datasets.

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Role-based access control (RBAC) model has gained significant attention in cybersecurity in recent years. RBAC restricts system access only to authorized users based on the roles and regulations within an organization. The flexibility and usability of this model have encouraged organizations to migrate from traditional discretionary access control (DAC) models to RBAC. However, this transition requires accomplishing a very challenging task called role mining in which users' roles are generated from the existing access control lists. Although various approaches have been proposed to address this NP-complete problem in the literature, they suffer either from low scalability such that their execution time increases exponentially with the input size, or they rely on fast heuristics with low optimality that generate too many roles. In this paper, we introduce a highly scalable yet optimal approach to tackle the role mining problem. To this end, we utilize a non-negative rank reduced matrix decomposition method to decompose a large-scale user-permission assignment into two constitutive components, i.e. the user-role and role-permission assignments. Then, we apply a thresholding technique to convert real-valued components into binary-valued factors. We employ various access control configurations and demonstrate that our proposed model is able to effectively discover the latent relationship behind the user-permission data even with large datasets.

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