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Mining Least Privilege Attribute Based Access Control Policies

Creating effective access control policies is a significant challenge to many organizations. Over-privilege increases security risk from compromised credentials, insider threats, and accidental misuse. Under-privilege prevents users from performing their duties. Policies must balance between these competing goals of minimizing under-privilege vs. over-privilege. The Attribute Based Access Control (ABAC) model has been gaining popularity in recent years because of its advantages in granularity, flexibility, and usability. ABAC allows administrators to create policies based on attributes of users, operations, resources, and the environment. However, in practice, it is often very difficult to create effective ABAC policies in terms of minimizing under-privilege and over-privilege especially for large and complex systems because their ABAC privilege spaces are typically gigantic. In this paper, we take a rule mining approach to mine systems' audit logs for automatically generating ABAC policies which minimize both under-privilege and over-privilege. We propose a rule mining algorithm for creating ABAC policies with rules, a policy scoring algorithm for evaluating ABAC policies from the least privilege perspective, and performance optimization methods for dealing with the challenges of large ABAC privilege spaces. Using a large dataset of 4.7 million Amazon Web Service (AWS) audit log events, we demonstrate that our automated approach can effectively generate least privilege ABAC policies, and can generate policies with less over-privilege and under-privilege than a Role Based Access Control (RBAC) approach. Overall, we hope our work can help promote a wider and faster deployment of the ABAC model, and can help unleash the advantages of ABAC to better protect large and complex computing systems.