Role recommender-RBAC: Optimizing user-role assignments in RBAC

Rao, K. Rajesh and Nayak, Ashalatha and Ray, Indranil Ghosh and Rahulamathavan, Yogachandran and Rajarajan, Muttukrishnan (2021) Role recommender-RBAC: Optimizing user-role assignments in RBAC. Computer Communications, 166. pp. 140-153. ISSN 0140-3664

[img] PDF
10822.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy
Official URL: http://www.elsevier.com/locate/comcom

Abstract

In a rapidly changing IT environment, access to the resources involved in various projects might change randomly based on the role-based access control (RBAC) system. Hence, the security administrator needsm to dynamically maintain the role assignments to users for optimizing user-role assignments. The manual updation of user-role assignments is prone to error and increases administrative workload. Therefore, a role recommendation model is introduced for the RBAC system to optimize user-role assignments based on user behaviour patterns. It is shown that the model automatically revokes and refurbishes the user-role assignments by observing user access behaviour. This model is used in the cloud for providing Role-Assignment-as-a-Service to optimize the cost of built-in roles. Several experiments are conducted to verify the proposed model using the Amazon access sample dataset. The experimental results show that the efficiency of the proposed model is 50% higher than the state-of-the-art.

Item Type: Article
Uncontrolled Keywords: Access control Cloud computing Hidden Markov model RBAC Role recommendation
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 29 Oct 2021 06:33
Last Modified: 29 Oct 2021 06:33
URI: http://eprints.manipal.edu/id/eprint/157576

Actions (login required)

View Item View Item