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Recently Deep Learning based Access Control (DLBAC) model has been developed to reduce the burden of access control model engineering on a human administrator, while managing accurate access control state in large, complex, and dynamic systems. DLBAC utilizes neural networks for addressing access control requirements of a system based on user and resource metadata. However, in today’s rapidly evolving, dynamic, and complex world with billions of connected users and devices, there are various environmental aspects in different application domains that affect access control rights and decisions. While Attribute-Based Access Control (ABAC) have captured environmental factors through environmental attributes, DLBAC still lacks the capabilities of capturing any environmental factors and its use in access control decision making. In this paper, we propose an environment aware deep learning …