### Bot-IoT Dataset  
Table 6 and Figure 8 present different measures to evaluate our model on the Bot-IoT dataset. The full dataset scored 99.98% ACC, 100% precision, and 99.99% recall. Our feature selection model eliminates many features and maintains the same performance on the ACC, precision, and recall. We integrated the MCC measure to prove our model worked well with an imbalanced dataset like the Bot-IoT. As proof, the entire dataset scored 28.47% on MCC, seeing that the model could not adequately recognize the typical instances due to its few numbers in the dataset. On the other hand, our model helped reduce this issue's impact on the results. The ten and then the three selected features scored 83.83% and 93% MCC, respectively, which are way better than the 28.47% scored for the entire dataset. Consequently, the three chosen features achieved the higher MCC by distinguishing between regular instances and attacks well. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Features** & **ACC (\%)** & **Precision (\%)** & **Recall (\%)** & **MCC (\%)** \\ \hline Full Dataset & 99.98 & 100 & 99.99 & 28.47 \\ \hline
10 Features & 99.99 & 100 & 99.99 & 83.83 \\ \hline
3 Features & 99.99 & 100 & 99.98 & 93.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance metrics on the Bot-IoT dataset. Figure 8: Different metrics measure the model performance on the Bot-IoT dataset. Figure 7: The four selected features from the NSL-KDD dataset confusion matrix. Figure 9 illustrates a comparison histogram of the confusion matrix measures TP, TN, FP, and FN scored by the Bot-IoT dataset. Based on the figure, our feature selection method helps the model to perform well in maintaining superior performance when detecting positive instances. It helps boost the detection of regular cases. Where the full feature of the NSL-KDD dataset scored 8.1% on TN, the ten selected features scored 70%, and the three chosen elements scored 86%. Figures 10-12 depict the four measures, including TP, TN, FP, and FN, of the full Bot-IoT dataset's confusion matrix, ten selected features, and three selected features. Figure 10 shows the confusion matrix of the full Bot-IoT dataset. The model scored 8.1% TN, 92.9% FN, 0% FP, and 100% TP. As the results show, our model performed poorly in detecting the negative instances, which could lead to blocking many friendly data and packets. These unsatisfactory results were due to the imbalance between the standard and attack instances in the Bot-IoT dataset. To reduce the impact of this imbalance on the results, we applied the feature selection as mentioned above. Figure 10: The full Bot-IoT dataset confusion matrix. Figure 9: The Bot-IoT dataset confusion matrix comparison. We obtained the outcomes depicted in Figure 11 after executing the feature selection algorithm and choosing the ten best-performing features. The confusion matrix for the selected ten features from the Bot-IoT dataset is shown in this figure. The model showed significant improvement in detecting the negative instances with 70% TN and 30% FN and maintained the excellent performance of the entire dataset in distinguishing the positive samples with 0% FP and 100% TP. We again applied the feature selection to see if we could reduce the features and gain or maintain the same results. Thus, we obtained the results shown in Figure 12.  
Figure 12 describes the confusion matrix of the three selected features from the Bot-IoT dataset as the last point before we started to lose our good results, even with testing every two components together. So, our model showed exciting results in detecting the negative instances with 86% TN and only 14% FN and maintained the outstanding performance of the entire dataset and ten selected features when distinguishing the positive examples with 0% FP and 100% TP. Our model showed efficiency in overtaking the impact of the Bot-IoT imbalance, as shown in Figure 12. To test the performance on a more balanced dataset, we evaluated our model on the NSL-KDD dataset, and the results remain very courageous.