Intelligent Agent-Based RBAC Model to Support Cyber Security Alliance Among Multiple Organizations in Global IT Systems

Abstract

Secure information sharing and collaboration among multiple organizations in global IT system require appropriate access control. Role Based Access Control (RBAC) is one of them but it lacks to fulfill the dynamicity of global IT systems as well as real world concept mapping into digital world. These issues are the main focus of this paper and a solution is proposed as Intelligent Agent-based RBAC (IA-RBAC) which discover the roles based upon real world concepts of occupations and job titles in any organization. Intelligent agents used to build associations among permissions and tasks. Supervised learning is used to train agents for classification of roles according to the set of assigned tasks. Knowledge is stored in the form of ontologies for roles, permissions, policies and constraints. Ontology-based Agent Communication Language (ACL) is used for collaboration among intelligent agents. The functionality of proposed model is demonstrated by a case study.