## 4 Results
### Micro-Experiments  
**Effect of Feature Normalization:** Table 19 illustrates the impact of feature normalization on the DNN, SVM, and RF across the CICIDS-2017, RoEduNet-SIMARGL2021, and NSL-KDD datasets. Notably, we observe that feature normalization consistently leads to improved accuracy across all three datasets.  
Upon examining the results presented in Table 19, we find that feature normalization generally enhances accuracy for the DNN, SVM, and RF models employed in this micro-experiment (e.g., an increase in the accuracy of the DNN model on the RoEduNet-SIMARGL2021 dataset from 0.62 to 0.99) across the various datasets under consideration. It is worth mentioning that this trend holds true for the majority of cases (46 out of 54 cases), indicating a notable improvement in model performance facilitated by feature normalization. However, there are a few exceptions (8 cases out of 54) where normalization does not yield enhanced performance, which is particularly evident for the NSL-KDD dataset.  
Given these findings, we advocate for feature normalization across all our AI model results within the scope of _our framework_, as discussed in the paper.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**AI Model—DNN** & **Acc** & **Prec** & **Rec** & **F1** & **Bacc** & **Mcc** & **AucRoc** \\ \hline CICIDS-2017 (No Norm.) & 0.94 & 0.80 & 0.80 & 0.80 & 0.88 & 0.77 & 0.80 \\ CICIDS-2017 (Norm) & **0.95** & **0.83** & **0.83** & **0.83** & **0.90** & **0.80** & **0.96** \\ RoEduNet-SIMARGL2021 (No Norm.) & 0.62 & 0.24 & 0.24 & 0.24 & 0.43 & 0.13 & 0.50 \\ RoEduNet-SIMARGL2021 (Norm) & **0.99** & **0.99** & **0.99** & **0.99** & **0.99** & **0.98** & **0.99** \\ NSL-KDD (No Norm.) & 0.76 & 0.47 & 0.45 & **0.62** & **0.70** & **0.57** & 0.55 \\ NSL-KDD (Norm) & **0.88** & **0.51** & **0.48** & 0.46 & 0.69 & 0.40 & **0.69** \\ \hline
**AI Model—SVM** & **Acc** & **Prec** & **Rec** & **F1** & **Bacc** & **Mcc** & **AucRoc** \\ \hline CICIDS-2017 (No Norm.) & **0.98** & **0.92** & **0.92** & **0.95** & **0.90** & 0.50 \\ CICIDS-2017 (Norm) & **0.98** & **0.92** & **0.92** & **0.92** & **0.95** & **0.90** & **0.60** \\ RoEduNet-SIMARGL2021 (No Norm.) & 0.47 & 0.21 & 0.21 & 0.21 & 0.41 & \(-\)0.18 & 0.50 \\ RoEduNet-SIMARGL2021 (Norm) & **0.75** & **0.62** & **0.62** & **0.62** & **0.72** & **0.44** & **0.49** \\ NSL-KDD (No Norm.) & 0.75 & 0.54 & **0.98** & **0.70** & 0.65 & **0.46** & 0.67 \\ NSL-KDD (Norm) & **0.87** & **0.60** & 0.43 & 0.43 & **0.66** & 0.36 & **0.70** \\ \hline
**AI Model—RF** & **Acc** & **Prec** & **Rec** & **F1** & **Bacc** & **Mcc** & **AucRoc** \\ \hline CICIDS-2017 (No Norm.) & **0.99** & **0.99** & **0.99** & **0.99** & **0.99** & **0.99** & 0.56 \\ CICIDS-2017 (Norm) & **0.99** & **0.99** & **0.99** & **0.99** & **0.99** & **0.99** & **0.61** \\ RoEduNet-SIMARGL2021 (No Norm.) & 0.66 & 0.29 & 0.29 & 0.29 & 0.47 & 0.06 & 0.50 \\ RoEduNet-SIMARGL2021 (Norm) & **0.98** & **0.95** & **0.95** & **0.95** & **0.97** & **0.95** & **0.51** \\ NSL-KDD (No Norm.) & 0.80 & 0.75 & 0.38 & **0.67** & 0.67 & **0.67** & 0.60 \\ NSL-KDD (Norm) & **0.84** & **0.78** & **0.42** & 0.36 & **0.68** & 0.32 & **0.62** \\ \hline \hline \end{tabular}
\end{table}
Table 19: The effect of feature normalization on the performance of AI models for the three datasets in XAI-IDS.