## 4 Related Work
### Machine Learning-Based Policy Decision Point Enforcement
in [19] suggested a decision-making-based Boosting Window (BW) algorithm for constructing an access control knowledge graph using user and resource attributes with approximately 89.64 percent performance. However, it did not address access decision time. Even though machine learning techniques have improved the dynamics of access control decision-making, there are still issues with access decisions, such as PDP bottlenecks, delayed access decision times and the inability to respond to unexpected access, which means access requests whose attributes are not predefined cannot be granted. This paper will propose a supervised machine learning technique for generating dynamic access control decisions with high performance and reduced time costs. When a conflict or PDP bottleneck occurs, this technique transforms the problem of policy decision points (PDPs)into an easily understood binary that accepts or denies access requests. The paper will investigate how prior research has influenced access control decision-making based on essential criteria for making the correct decision in the shortest amount of time and with the highest level of performance. Table 1 shows the evaluation of policy decision points (PDPs) based on the criteria of dynamic, distributed, conflict-free, performance, access decision time and machine learning use. * **Dynamic:** dynamic decision-making is dependent decision-making that occurs in a changing environment over time, whether due to the decision-makers actions or external factors outside the decision-makers control. * **Distributed:** a distributed access control that distributes access decisions to multiple nodes and responds to access requests in parallel to reduce time costs and achieve high levels of privacy, transparency and security [21]. It is a lightweight access control system that reduces access control overhead by making decisions with a single login based on attributes. In addition, distributed systems result in decentralised operations and administrative and organisational complications [22]. * **Free-Conflict:** to prevent the decision-making process of access control from issuing decisions due to policy conflicts caused by increased rules, the number of entries, massive simultaneous access requests and unexpected requests with attributes that differ from PAP and PIP policies [23]. * **PDP Performance:** perform the correct, accurate decision-making to issue the access request, either allow or deny, in the shortest time possible while keeping the system secure from incorrect decision-making [24]. * **Access Decision Time:** the processing time for access requests to enforce an access decision. If it is considerable, it will have a damaging effect on security access control [25]. * **ML-Based:** decision-making relies on ML technologies to determine whether to grant or deny access. According to Table 1, previous research efforts to enhance decision-making were limited and did not consider all criteria or access control issues. Therefore, this study will enhance access control decision-making by applying ML technology to make permission decisions that are distributed, accurate and dynamic, with a low access response time and high performance.