In a rapidly changing IT environment, access to the resources involved in various projects might change randomly based on the role-based access control (RBAC) system. Hence, the security administrator needs to dynamically maintain the role assignments to users for optimizing user-role assignments. The manual updation of user-role assignments is prone to error and increases administrative workload. Therefore, a role recommendation model is introduced for the RBAC system to optimize user-role assignments based on user behaviour patterns. It is shown that the model automatically revokes and refurbishes the user-role assignments by observing user access behaviour. This model is used in the cloud for providing Role-Assignment-as-a-Service to optimize the cost of built-in roles. Several experiments are conducted to verify the proposed model using the Amazon access sample dataset. The experimental results show that the efficiency of the proposed model is 50% higher than the state-of-the-art.

在瞬息万变的IT环境中，基于角色的访问控制（RBAC）系统可能会随机更改对各个项目中涉及的资源的访问。因此，安全管理员需要动态维护对用户的角色分配，以优化用户角色分配。手动更新用户角色分配容易出错，并且会增加管理工作量。因此，为RBAC系统引入了角色推荐模型，以基于用户行为模式优化用户角色分配。结果表明，该模型通过观察用户访问行为来自动撤销和翻新用户角色分配。此模型在云中用于提供“角色分配即服务”，以优化内置角色的成本。使用Amazon访问样本数据集进行了一些实验，以验证提出的模型。实验结果表明，提出的模型的效率比最新模型高50％。