Research Article
Supervised Learning-Based Approach Mining ABAC Rules from Existing RBAC Enabled Systems
@ARTICLE{10.4108/eetsis.v5i16.1560, author={Gurucharansingh Sahani and Chirag Thaker and Sanjay Shah}, title={Supervised Learning-Based Approach Mining ABAC Rules from Existing RBAC Enabled Systems}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={10}, number={1}, publisher={EAI}, journal_a={SIS}, year={2022}, month={9}, keywords={Attribute-based Access Control (ABAC),, Role-Based Access Control (RBAC), Mining ABAC Rule, Supervised Machine Learning}, doi={10.4108/eetsis.v5i16.1560} }- Gurucharansingh Sahani
Chirag Thaker
Sanjay Shah
Year: 2022
Supervised Learning-Based Approach Mining ABAC Rules from Existing RBAC Enabled Systems
SIS
EAI
DOI: 10.4108/eetsis.v5i16.1560
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.
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