## 4 Related Work
### Machine Learning-Based Policy Decision Point Enforcement  
It has been determined that machine learning positively affects access control and has tremendous potential for further development. Machine learning is increasingly being used for security purposes. Furthermore, access control based on machine learning has been implemented in various contexts, including policy decision-making, verification and testing,administration and monitoring and policy extraction. This research will focus primarily on how machine learning is used in access decision-making, how it improves system performance and how it resolves related problems in the traditional PDP of access control. In addition, research efforts in [13; 14; 15; 16; 17] employed the machine learning technique for more accurate access control decision-making. These systems decide access based on a trained machine learning model that uses information about users and resources to determine whether access requests should be granted or denied. Furthermore, a machine-learning model can be trained to make future access decisions based on the metadata and characteristics of users and resources. [PERSON] et al. [14] introduced a new method of access control named \"time constraint access control\" that can be used to implement policies that restrict user access based on the passage of time. The authors employ support vector machines (SVM) to implement the proposed technique and divide the procedures into three distinct phases: (1) input pattern modification; (2) SVM training; and (3) authority determination. First, the system administrator selects each user's unique login time and password as part of the training data. Then, using only their passwords and the timestamps of their system logins, trained SVMs can classify users into groups and grant them the appropriate security privileges. Instead of relying on predefined access controls, trained SVMs are used to make security decisions. However, this access decision solution is limited to time-limited access regulations to implement the access decision and needs to consider the dynamics of policy problem-solving. [PERSON] et al. [13] suggested a method for determining access control decisions based on access history. The authors employed a variety of ML techniques, including support vector machines (SVM), decision trees (DT), random forests (RF) and multi-layer perceptrons (MLPs). The solution does not address PDP issues. [PERSON] et al. [16] provide a trust-based mechanism for granting access to cloud resources based on authorization behaviour and history. In addition, this approach considers additional criteria, such as user behaviour, fake requests, unapproved requests, prohibited requests, and range specifications. The solution does not address performance bottlenecks or unexpected access requests. [PERSON] et al. [17] implemented a Risk access control (RAAC) system that checks the requester, calculates risk and acts on access decisions accordingly, considering access time, location, frequency of requests and resource sensitivity. The experiments used RF technology and a hidden two-layer neural network on a hospital management system (HMS). [PERSON] et al. [15], an adaptive ABAC policy-learning approach to automate decision-making. The authors present a method that predicts policies based on the attributes of access requests and then executes the access decision using an Epsilon Greedy-based reinforcement learning algorithm. However, a PDP performance of around 59% was unsatisfactory and this method did not consider access response time. [PERSON] et al. in [18] introduced \"deep learning-based access control\" (DLBAC) that uses a black-box neural network to decide access decisions with an Accuracy of 88.5 percent. [PERSON] et al. in [19] suggested a method for identifying patterns in access logs and deriving ABAC authorization rules from these patterns using k-mean and k-mode, an unsupervised learning algorithm with a high level of performance. However, the access decision time is prolonged. [PERSON] et al.