City Research Online

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

Rao, K. R., Nayak, A., Ray, I. G. , Rahulamathavan, Y. & Rajarajan, M. ORCID: 0000-0001-5814-9922 (2021). Role recommender-RBAC: Optimizing user-role assignments in RBAC. Computer Communications, 166, 140`-153. doi: 10.1016/j.comcom.2020.12.006

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 needs 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.

Publication Type: Article
Additional Information: © 2020. This article has been published in Computer Communications by Elsevier, doi: https://doi.org/10.1016/j.comcom.2020.12.006. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: Access control, Cloud computing, Hidden Markov model, RBAC, Role recommendation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Electrical & Electronic Engineering
[img]
Preview
Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (1MB) | Preview

Export

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login