Multilevel Access Control model for highly Scalable Cloud deployments with Contextual extensions

Effective access control is crucial for modern cloud deployments. Common methods like Discretionary Access Control (DAC), Role-Based Access Control (RBAC), and Mandatory Access Control (MAC) help implement access control policies for different types of access requests. However, current models that incorporate these methods are often either too complex or provide lower Quality of Service (QoS) in large-scale deployments. Additionally, their efficiency is limited by the number of access control points, which hampers scalability under real-time loads.To address these issues, we propose a novel multilevel access control model designed for highly scalable cloud deployments with contextual extensions. This model uses MAC for each new user by redirecting requests to centrally controlled resources, thus converging all endpoints to a single entity and improving control efficiency. The MAC entity is managed via an efficient DAC model that establishes rules for RBAC requests. Additionally, RBAC is enhanced to include contextual access control features such as location-based restrictions, IP-based restrictions, and request-consistency-based restrictions.Due to these operations, the proposed model is able to improve the efficiency of access control by 9.5%, while improving the speed of access by 3.5%, and reduce computational overloads by 8.3% when compared with existing access control models on large-scale cloud deployments. This performance was also observed to be consistent even under heterogenous access-control attacks.