Supervised Learning-Based Approach Mining ABAC Rules from Existing RBAC Enabled Systems

Abstract

Attribute-Based Access Control (ABAC) is an emerging access control model. It is the more flexible, scalable, and most suitable access control model for today’s large-scale, distributed, and open application environments. It has become an emerging research area nowadays. However, Role-Based Access Control (RBAC) has been the most widely used and general access control model so far. It is simple in administration and policy definition. But user-to-role assignment process of RBAC makes it non-scalable for large-scale organizations with a large number of users. To scale up the growing organization, RBAC needs to be transformed into ABAC. Transforming existing RBAC systems into ABAC is complicated and time-consuming. In this paper, we present a supervised machine learning-based approach to extract attribute-based conditions from the existing RBAC system to construct ABAC rules at the primary level and simplify the process of the transforming RBAC system to ABAC.