## 8 Discussion  
This section discusses the paper's contributions and their advantages over existing solutions. This research enhanced the decentralised, adaptable and dynamic policy decision-making point (PDP) in access control, making it compatible with BYOD, IoT and distributed systems. To begin, BYOD policies and access control technology are essential for defining restrictions and rights to prevent unauthorised users and ensuring the confidentiality, availability, data integrity and privacy of users and their data within an organisation. However, security BYOD and access control policies have limitations. Therefore, this study aimed to enhance conventional policies and access control systems. Firstly, policy decision point (PDP) employs predefined rules that must be automatically updated, causing policy conflicts, high maintenance costs and inferior performance. System administrators create complex rules to circumvent this limitation with multiple conditions, restrictions and permissions, resulting  
Figure 9: Analyze time under different policy scales. in access control issues. Secondly, the policy decision point (PDP) employs a centralised architecture, a multi-valued logic system and domain-specific equivalent operators. Thus, policy size and entity count have a negative impact on its performance, resulting in an increase in access decision-time costs. Thirdly, the direct communication between the PDP and the policy information point (PAP) raises privacy concerns. Previous studies offered many solutions, yet some still needed to employ ML and make decisions based on predefined rules that could not be automatically updated when user or resource attributes changed. Some of them employed ML techniques but needed to produce adequate outcomes in terms of PDP performance or access decision time. Consequently, the main contribution of this paper is that it enhanced policy decision points (PDPs) to be highly adaptable and dynamic to adapt to changes in subject or object attributes, thus addressing policy conflicts and performance bottlenecks. This is achieved through ML technology that transforms unexpected access requests and any access request problems into a binary classification, either permit or deny, with high performance and fast access decision times, rather than the predefined rules and logical PDP in traditional access control. Furthermore, to be compatible with the BYOD structure, the policy decision point of access control was made into a distributed PDP by implementing the random forest algorithm. This algorithm distributes the access decision across multiple nodes to reduce time costs and ensures that if one node is compromised, it cannot compromise the others. Distributed access decisions utilised a conditional and cascade architecture, in contrast with the centralised architecture of conventional access control. The proposed method binary-classified the issues associated with the policy decision point and responded to the access request based on its characteristics without communicating the policy information point (PAP). It did not communicate directly with the core policy set. The policy scope and the number of simultaneous access requests did not affect the access decision-making process. Additionally, the solution reduced response time for access requests and made the system more flexible and lightweight. As described in Section 5, the proposed method was implemented in several sequential steps: dataset balancing, attribute and feature dimension reduction, a training and testing model and a decision-making model. To evaluate the efficacy of the methodology, a real-world Amazon access control policy dataset [15] was employed. Furthermore, several experiments for five different techniques were conducted to compare the performance of different algorithms before and after the processing of balancing data.