## 4 Experimental Results
### Validate Data Augmentation Mechanisms Using ToN-IoT Dataset
However, the classification performance of each class in precision, recall, and F1 scores needs to be improved. Figure 8: (**a**) The accuracy of 10 classes in the ToN-IoT dataset based on the MLP classifier and (**b**) the loss of 10 classes in the ToN-IoT dataset based on the MLP classifier. Figure 7: (**a**) The accuracy of five classes in the ToN-IoT dataset based on the MLP classifier and (**b**) the loss of five classes in the ToN-IoT dataset based on the MLP classifier. The third experiment is based on the training results of the MLP classifier in the second experiment and uses the proposed data augmentation method to improve the classification performance of each class. For the four classes of DoS, DDoS, Password, and Mitm, which have poor MLP classifier performance, GANs are used for data augmentation, respectively, to improve classification performance. The loss values of these four classes of generators and discriminators during training and verification are shown in Figure 9. In Figure 9a, the GANs for the \"DoS\" class completed their training after 6352 epochs, with the generator's loss reaching 0.7456 and the discriminator's loss at 0.6522. In Figure 9b, the GANs handling the \"DDoS\" class stopped training at the 5886 th epoch, with the generator's loss at 0.7312 and the discriminator's loss at 0.6803. Moving to Figure 9c, GANs dedicated to the \"Password\" class concluded their training at the 6196 th epoch, with the generator's loss measuring 0.7466 and the discriminator's loss at 0.6982. Finally, in Figure 9d, the GANs responsible for the \"Mitm\" class finalized their training after 6151 epochs, resulting in a generator's loss of 0.7029 and a discriminator's loss of 0.6834. Add the augmented data to the original dataset to expand it into a new dataset. Use this new dataset for the training of the MLP classifier classification. The training accuracy and loss values are shown in Figure 10. The accuracy of both training and validation remains at 98%, while the loss value is only about 3.7%. The performance of each class is shown in Table 11. The classification performance of all 10 classes exceeds 90% in precision, recall, and F1-score. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Precision** & **Recall** & **F1-Score** \\ \hline DoS & 84.19\% & 78.21\% & 81.09\% \\ DDoS & 80.02\% & 97.06\% & 87.72\% \\ Backdoor & 100\% & 100\% & 100\% \\ Password & 98.22\% & 88.26\% & 92.97\% \\ Scanning & 100\% & 99.53\% & 99.76\% \\ Injection & 100\% & 99.23\% & 99.61\% \\ Mitm & 96.31\% & 67.23\% & 79.18\% \\ Normal & 100\% & 100\% & 100\% \\ Ransomware & 100\% & 100\% & 100\% \\ Xss & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The performance of MLP classification using 10 classes in the ToN-IoT dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Precision** & **Recall** & **F1-Score** \\ \hline DoS & 92.68\% & 90.16\% & 91.40\% \\ DDoS & 93.27\% & 96.26\% & 94.74\% \\ Backdoor & 100\% & 100\% & 100\% \\ Password & 99.31\% & 92.19\% & 95.62\% \\ Scanning & 98.12\% & 97.26\% & 97.69\% \\ Injection & 100\% & 99.23\% & 99.61\% \\ Mitm & 97.56\% & 91.33\% & 94.34\% \\ Normal & 100\% & 100\% & 100\% \\ Ransomware & 99.06\% & 97.14\% & 98.09\% \\ Xss & 99.92\% & 98.29\% & 99.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The performance of MLP classification using 10 classes in the ToN-IoT dataset.