## 4 Experimental Results
### Validate Data Augmentation Mechanisms Using ToN-IoT Dataset  
In the case of using the ToN-IoT dataset [33; 34], the effectiveness and accuracy of the proposed data augmentation-based classification method were validated. The number of records in each class in the ToN-IoT dataset is shown in Table 8. As the fields in various publicly available datasets vary slightly, the following features were selected for training in this dataset: source IP, destination IP, transmission protocol, duration time, source bytes, destination bytes, number of missing bytes, number of original packets, total length of IP header field of source systems, number of destination packets, and total length of IP header field of destination systems. Data pre-processing is described in Section 3.2. There are 10 classes in the dataset, including 9 attack classes and 1 normal class. The first experiment was to select only five classes (DoS, DDoS, Backdoor, Password, and Scanning) with sufficient data in the dataset to train the MLP classifier. The training parameters are as set in the previous section. The experimental results for the accuracy and loss of the proposed classification method are shown in Figure 7. In Figure 7a, the accuracy is relatively low in the early stages of training and verification, but as the training rounds increase, the accuracy can remain above 90%. In Figure 7b, the losses for training and validation are close to 0.16. The performance of each class is shown in Table 9. The experimental results show that the classification performance of the five classes has been significantly improved. That is, the accuracy, precision, recall, and F1-score are all higher than 90%. The training time and testing time are 38 s and 1.42 s, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Precision** & **Recall** & **F1-Score** \\ \hline DoS & 100\% & 99\% & 99.50\% \\ DDoS & 100\% & 100\% & 100\% \\ Reconnaissance & 96.06\% & 94.73\% & 95.39\% \\ Theft & 92.22\% & 90.69\% & 91.45\% \\ Normal & 99.81\% & 92.07\% & 95.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The performance of MLP-based IoT attack classification with data augmentation for GANs. \begin{table}
\begin{tabular}{c c} \hline \hline
**Classes** & **Number of Records** \\ \hline DoS & 20,000 \\ DDoS & 20,000 \\ Backdoor & 20,000 \\ Password & 20,000 \\ Scanning & 20,000 \\ Injection & 20,000 \\ Mitm & 985 \\ Normal & 289,827 \\ Ransomware & 19,901 \\ Xss & 20,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The number of records in each class in the ToN-IoT dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Precision** & **Recall** & **F1-Score** \\ \hline DoS & 94.28\% & 93.23\% & 93.75\% \\ DDoS & 91.01\% & 99\% & 94.83\% \\ Backdoor & 98.21\% & 90.05\% & 93.95\% \\ Password & 100\% & 100\% & 100\% \\ Scanning & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The performance of MLP classification using five classes in the ToN-IoT dataset. The second experiment uses 10 data classes in the ToN-IoT dataset to train the MLP classifier. The training parameters are as set in the previous section. The accuracy and loss of the experimental results are shown in Figure 8. In Figure 8a, the accuracy is relatively low in the early stages of training and verification, but as the training rounds increase, the accuracy can remain above 97%. In Figure 8b, the losses for training and validation are close to 0.05. The performance of each class is shown in Table 10. The experimental results can find that the accuracy, recall rate, and F1-score of \"DDoS\", \"DoS\", and \"Password\" among the original five classes have dropped significantly. The accuracy of the \"Mitm\" classification is 96%, but the recall is only about 67%. The training time and testing time are 228 s and 5.7 s, respectively. Obviously, when the number of data in each class among the 10 classes is greatly different, the overall classification accuracy of the MLP classifier can reach 97%.