## 6 Experimental Results
### ToN-IoT Dataset  
The previously described ToN-IoT dataset contains heterogeneous data from Win7, Win10, Network, and IoT. We evaluated its efficiency using DenseNet and the Inception Time algorithm in a multi-class scenario. We used a DenseNet with a depth of 121 on the ToN-IoT dataset for evaluation, and divided it into 80% training and 20% testing sets. The experiments were based on the different features of the Win7, Win10, Network, and Win10-Network training models. As shown in Table 6, the results showed that the model achieved good results. The best accuracy was recorded for the Win10-Network model--up to 99.9%--and the lowest accuracy we obtained for Win10 was 97.7%. The results of Win7 and Network were 98.36% and 98.5%, respectively. It should be noted that we used a hyperparameter, a starting learning rate of 0.005, and a batch size of 64. Figure 5 shows the accuracy, loss, recall, precision, and F1-score of the DenseNet algorithm. Figure 6 shows the achieved training and validation accuracies after 29 Win7 epochs, 50 Win10 epochs, and 5 Win10-Network epochs in the ToN-IoT dataset--which consists of Win7, Win10, Network, and Win10-Network--using the DenseNet algorithm. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & \multicolumn{4}{c}{**Type**} \\ \hline DenseNet & Win7 & Win10 & Network & Win10–Network \\ Training & 22,693 & 28,780 & 17,582,906 & 859,003 \\ Testing & 5674 & 7195 & 4,395,726 & 214,751 \\ Features & 132 & 124 & 42 & 166 \\ Epoch of 50 & 29 & 50 & 1 & 3 \\ Inception time & 22,693 & 28,780 & 17,582,906 & 859,003 \\ Testing & 5674 & 7195 & 4,395,726 & 214,751 \\ Features & 132 & 124 & 42 & 166 \\ Epoch of 50 & 32 & 42 & 1 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The values of training and testing, the numbers of features used, and the values of the epochs for the Inception Time and DenseNet algorithms. Figure 5: The results of the DenseNet model of the dataset. We also used the Inception Time algorithm to examine the performance of the same ToN-IoT dataset with the same training and testing values and the prior features of Win7, Win10, and Network, as shown in Table 6. All of the results indicated the superiority of the Inception Time model over the DenseNet model, as shown in Figure 7. The best accuracy we obtained was 100% for the Win10-Network model, followed by Network, with an accuracy of 99.65%, while the lowest accuracy we obtained in Win10 was 98.3% and for Win7 it was 99.2%, with a starting learning amount of 0.001 and an end-learning amount of 0.001, while maintaining the same batch size of 64. Figure 8 shows the achieved training and validation accuracies after 32 Win7 epochs, 42 Win10 epochs, and 5 Win10-Network epochs in the ToN-IoT dataset--which consists of Win7, Win10, Network, and Win10-Network--using the Inception Time algorithm. Figure 6: (**a–c**) The training and validation accuracy, F1-score, loss, recall, and precision after training the DenseNet model on the ToN-IoT dataset. Figure 8: _Cont_. Figure 7: The results (Win7, Win10, Network, Win10–Network) of the Inception Time model of the ToN-IoT dataset. Weather, thermostat, motion lighting, Modbus, garage, GPS, and fridge are the seven categories of IoT in the ToN-IoT database. To assess the performance, we used the Inception Time technique in a multi-class scenario with six classes (i.e., DDoS, password, normal, backdoor, scan, and XXS) with all features enabled; the results demonstrated its correctness. We achieved the highest accuracy of 100% for weather, thermostat, and GPS, and the lowest value we received for the fridge was 99%. In contrast, the Modbus, motion lighting, and garage accuracies were 99.9%, 99.5%, and 99.4%, respectively. Figure 9 displays the actual findings for all categories of IoT in the ToN-IoT database using Inception Time. Experiments on the ToN-IoT database showed that the Inception Time model method outperformed DenseNet.