## 8 Discussion
These experiments compared the area under the curve (AUC) and the receiver operating characteristic (ROC) curve with the same estimation parameters to compare the performance of the different algorithms in terms of Accuracy, Recall, Precision and F1-score as well as the response time of the PDP to grant or deny access requests. According to the results of five machine learning approaches on the same data set before and after balancing the data, the random forest algorithm is the most effective compared to other techniques. It achieved the desired results in terms of performance (0.95%) and time (0.15 s) for an optimal AUC of (0.98%). Consequentially, the ROC performance of the algorithms was poor prior to the data balancing method. However, the KNN and SVM algorithms cannot provide an optimal and accurate access decision response in the PDP during access control. Following data processing (the balancing phase), the overall performance of the proposed algorithms improved in terms of the analyzable values of Accuracy, Recall, Precision and F1-score for the response policy decision point. PDP performance during access control and the R3 algorithm performs the worst, followed by SVM. Then, KNN, DT and RF have comparable performance and values. Overall, PDP-based ML has improved overall performance, flexibility and adaptability to satisfy the real-time access control requirements of massive access requests and policy sizes. The results showed that the random forest algorithm is better at access control decision-making because it can handle many access requests and complex resource-access request relationships.It can classify access users as authorised or unauthorised based on future access request and resource action policies. In addition, our experiment's random feature and data sample selection after processing prevented overfitting and improved model Accuracy. Since random forest is an ensemble method, it has more hyperparameters. Hyperparameters vary by decision tree. In other words, Random forest predicts using multiple decision trees. Random subsets of training data and features are used to build each random forest decision tree. This randomness reduces model overfitting and improves Accuracy. Furthermore, the effectiveness of the proposed solution was compared to previous research in the field. Researchers have proposed numerous solutions to improve access control decision-making but need more adaptability, dynamism and conflict resolution. Table 6 below summarises the most notable previous work that improved access control by employing the same dataset. First, [PERSON] et al. proposed deep learning-based access control (DLBAC), which makes access decisions using a black-box neural network [18]. According to the evaluation findings, DLBAC can suggest granting or rejecting authorization with 88.5% Accuracy and disregarding the effect of time on the speed of decision-making. Second, [PERSON] proposed a solution to improve the resolution decision-making based on the reinforcement-epsilon greedy algorithm, which improved time performance but was not satisfactorily efficient [15]. Finally, [PERSON] et al. in [19] proposed the Boosting Window (BW) algorithm as a decision-making algorithm for constructing an access control knowledge graph based on user and resource attributes. The solution enabled educational institutions such as universities and schools to control access by determining the access permissions between the access requester and the resource attributes. It achieved a moderately effective rate of 89.64 percent. This paper enhanced the policy decision point in access control based on supervised learning for resolving issues in traditional access control, such as policy conflicts and the efficient performance of PDP when many requests arrive simultaneously. The method involves handling unexpected access requests by converting every access request into a binary classification based on access permissions. The suggested solution is more efficient, flexible and dynamic.