## 7 Results Analysis
### Performance  
The receiver operating characteristic (ROC) curve illustrates the commensurate relationship between the number of positive predictions for policy decision point (PDP) outcomes and the number of samples. That is, the proposed method's accuracy in identifying the appropriate access requests that grant access to the organization's resources and the inappropriate access requests that are rejected immediately and dynamically. There are many reasons behind each predictions case. In general, false rejections, for example, If the model's features do not represent the data, the algorithm may generate less accurate results, leading to false denials. In addition, models that have been overfitted may need to generalize better to new data, leading to false rejections. Finally, the algorithm may make erroneous rejections if the model is not tuned or the hyperparameters are not optimized. For example, if the number of trees in the forest is insufficient, the model may not capture the data's underlying patterns, resulting in false rejections. Additionally, if the trees are too shallow or deep, the model may need to be more accurate with new data. Lastly, if the data set is noisy or contains outliers, the model may produce inaccurate outcomes and false rejections. In our experiment, the algorithm incorrectly rejected datasets. The number of samples in each class differs because the data set is unbalanced (the number of allowed policies in a policy set is greater than the number of denied policies). Based on the experimental results illustrated in Figures 4 and 5, it can be concluded that the receiver operating characteristic (ROC) curve is unchanged with the dissemination of the sample group. Consequently, the performance (ROC) of the algorithms was poor prior to the data balancing method. Furthermore, the k-nearest neighbours algorithm (KNN) and support vector machines (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. As seen in Table 3, the random forest (RF) algorithm evidenced an ideal AUC of 0.98. This approach enhances PDP's access control capabilities in response to access requests. This shows that the (RF) algorithm successfully implemented parallelism, effectively resolving issues caused by competing policies. It helped to alleviate the problem of bottleneck in the decision-making process by working in a distributed manner.These advantages significantly improved the overall security of the access control system. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Results** & \\ \hline
**Real Results** & **Allowed Access** & **Refused Access** \\ \hline
**Allowed Access** & \(D_{\text{AA}}\) & \(D_{\text{AR}}\) \\
**Refused Access** & \(D_{\text{RA}}\) & \(D_{\text{RR}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix of policy decision point PDP results. Finally, to evaluate the methodology, the same estimation parameters were generated and the Accuracy, Recall, Precision and F1-score values were compared for response policy decision point (PDP) performance during access control. As shown in Figure 6, logistic regression (LR) performs the worst, followed by support vector machines (SVM). Consequently, k-nearest neighbours (KNN), decision tree (DT) and random forest (RF) have comparable performance and values. In additional, Table 4 presents a performance before data processing (before the balancing data phase), and Table 5 examines the effectiveness of four primary metrics across various strategies and  
Figure 4: Analyzing ROC curve results before data balancing for all algorithms. Figure 5: Analyzing ROC curve results after data balancing for all algorithms.