Formal Approach to Detect and Resolve Anomalies while Clustering ABAC Policies

In big data environments with big number of users and high volume of data, we need to manage the corresponding huge number of security policies. Using Attribute-Based Access Control (ABAC) model to ensure access control might become complex and hard to manage. Moreover, ABAC policies may be aggregated from multiple parties. Therefore, they may contain several anomalies such as conflicts and redundancies, resulting in safety and availability problems. Several policy analysis and design methods have been proposed. However, most of these methods do not preserve the original policy semantics. In this paper, we present an ABAC anomaly detection and resolution method based on the access domain concept, while preserving the policy semantics. To make the suggested method scalable for large policies, we decompose the policy into clusters of rules, then the method is applied to each cluster. We prove correctness of the method and evaluate its computational complexity. Experimental results are given and discussed.