A Distributed Multi-User Access Control Middleware for Critical Applications

We present a novel access control middleware for distributed multi-user review. The system uses a fuzzy inference system trained on real world access control rules to evaluate and select reviewers as an extension to a more traditional access control system. The method is intended for high security need specific requests, as a supplement to regular access control methods. In this way, it models a multi-person access system common in mechanical controls like missile launches, bank vault opening, and other high criticality domains. The proposed method improves security by increasing the number of compromised users needed to perform an attack, taking advantage of situational awareness of peer users in a system. We evaluate the proposed system with an example implementation based on a real-world organization, and show that the system can be used to effectively implement a secure resource access control system. Our work contributes to the growing body of research into fuzzy-logic access control, ML in access control, and multi-user authentication systems.