Today's computing technologies offer and avail shared data and other resources making them available all to every user who needs them. Protecting shared resources in the distributed environment gives many critical security issues. Many access control models are used in the intrusion detection system to prevent dangerous attacks and malicious intruders. The attribute-based access control model (ABAC) is a boon to address today's complex security requirements due to the flexibility, efficiency, and granularity of the model. Despite the ABAC is proving well in overcoming the security issues, the errors in the policy sets cause critical security issues that should be resolved. The process of detecting and resolving anomalies, conflicts, or errors in every security rule is a time-consuming task. Hence the rules are clustered based on the similarity value of the rules. Existing approaches use the boolean function or logical-based analysis to measure the similarity value of security rules that do not measure accurately in heterogeneous datasets. The main aim of this paper is to propose an approach to measure the similarity value of pair of ABAC rules accurately. We aim to create databases to store and maintain the ABAC rules, and an individual domain for the name and value of each attribute to tackle the heterogeneous datasets. We proposed a stored function for measuring and returning similarity values. We extend our work by implementing the generated rules to describe the efficiency and performance of our approach. This work may help the researchers in measuring the accurate similarity value of rules.