## 1 Introduction  
Bring Your Own Device (BYOD), the concept of employees bringing their own devices to the workplace has become common in recent years due to the widespread use of smartphones, smart watches, laptops and tablets. BYOD strategies have produced substantial benefits for businesses, such as reduced organisational costs and enhanced productivity when employees use their devices. BYOD policies and access control technology are critical for defining restrictions and rights to prevent unauthorised users from accessingthe corporate network and its resources. Such controls attempt to prevent actions that could result in a security breach and ensure the confidentiality, availability, integrity, and privacy of users and their data within the organisation [1]. However, numerous obstacles exist to successfully implementing BYOD policies and access control solutions. Security is one of the most challenging obstacles to overcome. This difficulty is exacerbated by the dynamic nature of BYOD access control systems and by policy consistency [2]. For instance, in a BYOD scenario at a university, there are numerous devices for students and employees across multiple departments and colleges with numerous separate campus branches, as well as a considerable number of massive entities that access requests at the same time on the university server to request resources and services. The existing access control policies are rigid due to their reliance on specific attributes and a limited number of entities and policy sizes. However, the access control state could vary with minor changes to user and resource variables. Therefore, to manage this issue, system administrators rely on complicated rules with numerous criteria and limits or roles, many permissions, and the creation of increasingly detailed rules. These solutions raise many issues when creating increasingly fine-grained rules, such as policy conflicts, role explosion, bottlenecks in performance-access decision-making, and delays in access response that may cause system vulnerabilities and be exploited by attackers. The most significant challenges that motivated this paper are policy conflicts, which are bottlenecks at the policy decision point for making the right decision due to the dynamic, complex nature of the BYOD environment and changes in the attributes of the subject and the object. Therefore, it is necessary to design policy decisions based on dynamic and adaptable technologies that should not be subject to a static policy. The additional problem is that traditional access does not meet security requirements due to the decentralised architecture of the BYOD environment, which comprises multiple departments located on separate campuses, resulting in high access time costs, performance bottlenecks, and limited access requests and policy sizes. As a result, a system that distributes access decisions to multiple nodes and responds in parallel to access requests should be designed to reduce time costs, achieve elevated levels of privacy, transparency, and security and achieve decentralised operations, as well as resolve administrative and organisational complications. Distributed access is also known as \"lightweight access control\" since it reduces overhead by making single sign-on decisions based on attributes. Therefore, it is necessary to implement distributed access control, and access decision-making should be distributed across multiple node locations to reduce the time of access requests and ensure the privacy of access control policies. This paper enhances an access control decision-making model based on supervised machine learning to address the existing challenges. This is done by transforming access requests and PDP problems into a binary classification, either denial or approval. The solution employs supervised learning algorithms, but the random forest algorithm is the most accurate and effective at predicting the access decision. It also provides a distributed PDP of access control through a distributed implementation algorithm.