## 4 Performance Evaluation
### Experimental Results and Discussion
In contrast, our MSE model showcased significant improvements across most classes in the \(D1\) dataset, effectively mitigating the limitations observed in individual classifiers, as detailed in Table 7. Figure 6 presents the confusion matrices of MSE for multiclass classification in \(D1\), offering a comprehensive visual representation of the model's performance. Table 8 outlines the performance results of the MSE classifier alongside various individual classifiers for the \(D2\) dataset. The DT algorithm encountered challenges in identifying four attacks: Heartbleed; Infiltration; Web Attack--SQL Injection; and Web Attack--XSS. Hist-gradient boosting struggled with three attacks: Heartbleed, Infiltration, and Web Attack--SQL Injection. It also exhibited poor performance for Web Attack--Brute Force-- and Web Attack--XSS. RF failed to identify Infiltration and Web Attack--SQL Injection--with particularly low performance for Web Attack--XSS. The MLP also faced difficulties in identifying Infiltration and Web Attack--SQL Injection--but displayed better results for Web Attack--XSS--compared to other individual classifiers. In contrast, our proposed MSE model demonstrated substantial improvements, effectively identifying most attack classes in the \(D2\) dataset and addressing the limitations  
Figure 6: Multiclass confusion matrix for \(D1\). observed in individual algorithms, as depicted in Table 8. Figure 7 illustrates the confusion matrices of MSE for multiclass classification in \(D2\), providing a comprehensive visual insight into the model's performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Category of Attack** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{8}{*}{**DT**} & Benign & 99.54 & 98.69 & 99.11 \\  & Bot & 91.94 & 35.03 & 50.74 \\  & DdoS & 99.59 & 99.68 & 99.64 \\  & DcoS GoldenEye & 95.63 & 95.72 & 95.67 \\  & DcoS Hulk & 97.98 & 99.31 & 98.64 \\  & DcoS Slowhtptest & 95.17 & 98.63 & 96.87 \\  & DcoS slowloris & 96.75 & 95.08 & 95.90 \\  & FTP\(-\)Patator & 97.49 & 97.98 & 97.73 \\  & Heartbleed & 0 & 0 & 0 \\  & Infiltration & 0 & 0 & 0 \\  & PortScan & 86.38 & 96.08 & 90.98 \\  & SSH\(-\)Patator & 98.03 & 92.79 & 95.34 \\  & Web Attack\(-\)Brute Force & 64.86 & 87.70 & 74.57 \\  & Web Attack\(-\)SQL Injection & 0 & 0 & 0 \\  & Web Attack\(-\)XSS & 0 & 0 & 0 \\  & Benign & 99.54 & 98.53 & 99.03 \\  & Bot & 44.10 & 43.98 & 44.04 \\  & DdoS & 99.90 & 98.76 & 99.33 \\  & DoS GoldenEye & 96.98 & 85.96 & 91.14 \\  & DoS Flutik & 98.29 & 99.28 & 98.78 \\  & DoS Slowhtptest & 75.98 & 98.09 & 85.63 \\  & DoS slowloris & 91.48 & 96.46 & 93.91 \\  & FTP\(-\)Patator & 96.27 & 97.79 & 97.03 \\  & Heartbleed & 0 & 0 & 0 \\  & Infiltration & 0 & 0 & 0 \\  & PortScan & 89.82 & 95.34 & 92.50 \\  & SSH\(-\)Patator & 95.19 & 95.76 & 95.47 \\  & Web Attack\(-\)Brute Force & 22.60 & 88.70 & 36.03 \\  & Web Attack\(-\)SQL Injection & 0 & 0 & 0 \\  & & & & \\ Web Attack\(-\)SQL Injection & 0 & 0 & 0 \\  & & & & \\ Web Attack\(-\)XSS & 26 & 12 & 16.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation of multiclass classification results for \(D2\).