A Priority-based Approach for Detection of Anomalies in ABAC Policies using Clustering Technique

Cloud computing offers several computing services like storage, networks, hardware, and software. The most beneficial cloud service is cloud storage. The organization or large industries can store their big data in cloud storage on pay for usage scheme. As the big data are outsourced in a distributed cloud environment, securing and protecting the big data is essential. The various access control models, which consist of a set of security policies, are used generally to protect the outsourced data. Anomalies in the security policies dilute the efficiency of the access security model. Developing an efficient access control model to protect the data is a challenging and ongoing process. The primary goal of this paper is to analyze and detect the important anomalies in Attribute-Based Access Control-ABAC Policies. This paper presents an approach that uses Priority-level to avoid the conflict in ABAC Policies. This approach groups the rules of ABAC policies based on Priority-level and similarity with the clustering technique, and detect the anomalies in each cluster rather than all rules, which made this approach efficient.