## 6 Experimental Results
### Edge-Iiot Dataset  
This experiment sought to evaluate the performance of the Inception Time model for malware detection using the Edge-IIoT databset, which consists of 1,527,736 training data, 381,935 test data, and 91 features to ensure the reliability of the evaluation. We used four measures based on multiple classes: accuracy, precision, recall, and F1-score. Our best accuracy was 94.94%, as shown in Figure 11, which shows the percentage of attacks predicted properly for 15 classes of the confusion matrix illustrated in Figure 12, each of these was calculated independently, with a starting learning rate of 0.001, batch size of 64, and 50 epochs. The end-learning rate was 0.001.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Research** & **Model** & \multicolumn{6}{c}{**KNN**} \\ \hline  & Dataset & Fridge & Garage & GPS & Modbus & Motion Lighting & Thermostat & Weather \\ \cline{2-9}  & Accuracy & 0.99 & 1.00 & 0.88 & 0.97 & 0.54 & 0.60 & 0.81 \\ \cline{2-9}  & Precision & 0.99 & 1.00 & 0.89 & 0.97 & 0.34 & 0.56 & 0.81 \\ \cline{2-9}  & Recall & 0.99 & 1.00 & 0.88 & 0.97 & 0.59 & 0.61 & 0.81 \\ \cline{2-9}  & F1-score & 0.99 & 1.00 & 0.88 & 0.97 & 0.43 & 0.57 & 0.81 \\ \cline{2-9}  & & & & & & & LSTM & & \\ \hline Dataset & Fridge & Garage & GPS & Modbus & Motion Lighting & Thermostat & Weather \\ \hline Accuracy & 1.00 & 1.00 & 0.87 & 0.68 & 0.59 & 0.66 & 0.82 \\ \cline{2-9}  & Precision & 1.00 & 1.00 & 0.89 & 0.46 & 0.35 & 0.45 & 0.82 \\ \cline{2-9}  & Recall & 1.00 & 1.00 & 0.88 & 0.68 & 0.59 & 0.67 & 0.81 \\ \cline{2-9}  & F1-score & 1.00 & 1.00 & 0.88 & 0.55 & 0.44 & 0.54 & 0.80 \\ \hline  & & & & & & & & & \\ \hline  & Dataset & Fridge & Garage & GPS & Modbus & Motion Lighting & Thermostat & Weather \\ \cline{2-9}  & Accuracy & 0.9136 & 0.9314 & 0.92 & & 0.9216 & 0.9532 & 0.9669 \\ \cline{2-9}  & Precision & 0.89 & 0.90 & 0.92 & & 0.89 & 0.92 & 0.97 \\ \cline{2-9}  & Recall & 0.91 & 0.93 & 0.93 & & 0.92 & 0.95 & 0.97 \\ \cline{2-9}  & F1-score & 0.89 & 0.91 & 0.92 & & 0.90 & 0.94 & 0.96 \\ \cline{2-9}  & & & & & & & & & \\ \hline  & & & & & & & & & \\ \end{tabular}
\begin{tabular}{l l l l l l l l} \hline \hline
**Dataset** & Fridge & Garage & GPS & Modbus & Motion Lighting & Thermostat & Weather \\ \hline Accuracy & 0.9135 & 0.9314 & 0.94 & & 0.9211 & 0.9532 & 0.9680 \\ \cline{2-9}  & Precision & 0.98 & 0.90 & 0.95 & & 0.89 & 0.92 & 0.97 \\ \cline{2-9}  & Recall & 0.91 & 0.93 & 0.95 & & 0.92 & 0.95 & 0.97 \\ \cline{2-9}  & F1-score & 0.89 & 0.91 & 0.92 & & 0.90 & 0.94 & 0.96 \\ \hline  & & & & & & & & \\ \end{tabular}
\begin{tabular}{l l l l l l l l} \hline \hline
**Dataset** & Fridge & Garage & GPS & Modbus & Motion Lighting & Thermostat & Weather \\ \hline Accuracy & 0.  
Figure 13 shows the achieved training and validation accuracies; it could be observed that, after 34 epochs, the Edge-IIoT dataset used the Inception Time algorithm.  
Figure 11: The results of Inception Time and multi-class classification of the Edge-IIoT dataset.  
Figure 12: The confusion matrix for 15 classes in the Edge-IIoT–Inception Time validation: DDoS-HTTP, backdoor, DDoS-ICMP, DDoS-TCP, DDoS-UDP, fingerprinting, normal, password, port scanning, SQL-injection, uploading, XSS, vulnerability scanner, ransomware, and MITM; all values between 0.59 and 100.  
Table 9 compares our model to the previous study, demonstrating that the Inception Time model outperforms RF, SVM, KNN, and DNN in classifying multiple attacks, with the best result being 94.94% on the Edge-IIoT dataset.