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With the rapid advances in computing and information technologies,traditional access control models have become inadequate in terms of capturingfine-grained, and expressive security requirements of newly emergingapplications. An attribute-based access control (ABAC) model provides a moreflexible approach for addressing the authorization needs of complex and dynamicsystems. While organizations are interested in employing newer authorizationmodels, migrating to such models pose as a significant challenge. Manylarge-scale businesses need to grant authorization to their user populationsthat are potentially distributed across disparate and heterogeneous computingenvironments. Each of these computing environments may have its own accesscontrol model. The manual development of a single policy framework for anentire organization is tedious, costly, and error-prone.  In this paper, we present a methodology for automatically learning ABACpolicy rules from access logs of a system to simplify the policy developmentprocess. The proposed approach employs an unsupervised learning-based algorithmfor detecting patterns in access logs and extracting ABAC authorization rulesfrom these patterns. In addition, we present two policy improvement algorithms,including rule pruning and policy refinement algorithms to generate a higherquality mined policy. Finally, we implement a prototype of the proposedapproach to demonstrate its feasibility.

An Automatic Attribute Based Access Control Policy Extraction from Access Logs