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

Authors

  • Gurucharansingh Sahani Gujarat Technological University image/svg+xml
  • Chirag Thaker Lalbhai Dalpatbhai College of Engineering, Ahmedabad, India
  • Sanjay Shah Government College of Engineering, Rajkot, India

DOI:

https://doi.org/10.4108/eetsis.v5i16.1560

Keywords:

Attribute-based Access Control (ABAC),, Role-Based Access Control (RBAC), Mining ABAC Rule, Supervised Machine Learning

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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Published

07-09-2022

How to Cite

1.
Sahani G, Thaker C, Shah S. Supervised Learning-Based Approach Mining ABAC Rules from Existing RBAC Enabled Systems. EAI Endorsed Scal Inf Syst [Internet]. 2022 Sep. 7 [cited 2022 Dec. 3];10(1):e9. Available from: https://publications.eai.eu/index.php/sis/article/view/1560