## 6 Experimental Results
### Unsw-Nb15 Dataset  
We evaluated the performance of the Inception Time model on the UNSW-NB15 database to detect cyber-attacks. The training and test sets consisted of 2,032,037 and 508,010 data, respectively, in the case of 10 classes, and the feature number was 43. The Inception Time model was the subject of the first experiment, which yielded an accuracy of 98.4%. The percentage of multicategory attacks is shown in Figure 14. The correlation matrix in Figure 15 was used to calculate each category separately.  
Figure 13: The training and validation accuracy, F1-score, loss, recall, and precision after training the Inception Time model on the Edge-IIoT dataset.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Research** & **Class** & **Model** & **Accuracy** \\ \hline \multirow{4}{*}{[PERSON], et al. [22]} & \multirow{4}{*}{15} & RF & 80.83 \\  & & SVM & 77.61 \\ \cline{1-1}  & & KNN & 79.18 \\ \cline{1-1}  & & DNN & 94.67 \\ \cline{1-1} Our proposed method & 15 & Inception Time & 94.94 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of the results with previous studies using the Edge-IIoT dataset.  
Figure 16 shows the achieved training and validation accuracies after 36 epochs in the UNSW-NB15 dataset using the Inception Time algorithm.  
Figure 14: The Inception Time and multi-class results for the UNSW-NB15 dataset.  
Figure 15: The confusion matrix for 10 classes in the Inception Time validation using the UNSW-NB15 dataset: normal, exploits, generic, fuzzers, reconnaissance, DoS, backdoor, analysis, shellcode, and worms; all values between 0.53 and 99.  
The second experiment used Inception Time with a window size of six. The UNSW-NB15 dataset's network packets were used in the preprocessing stage to extract relevant features and turn individual packets into window sequences, where X_T = (t-window_size. t - 3 + t - 2 + t - 1 + T). In addition, y_t = y of the current window at time t. We evaluated its efficiency in multi-class scenarios with 43 features. The results showed a slight increase in accuracy to 98.6%. Figure 17 shows the percentage of multicategory attacks, and each category was calculated separately from the correlation matrix shown in Figure 18, with a starting learning rate of 0.005, batch size of 2048, and 50 epochs. The end-learning rate was 0.0015.  
Figure 16: The training and validation accuracy, F1-score, loss, recall, and precision after training the Inception Time model using the UNSW-NB15 dataset.  
Figure 17: The Inception Time and multi-class results of the UNSW-NB15 dataset with a window size of six.  
Figure 19 shows the achieved training and validation accuracies after 36 epochs in the UNSW-NB15 dataset using the Inception Time algorithm with window size.  
Figure 19: The training and validation accuracy, F1-score, loss, recall, and precision after training the Inception Time model with a window size of six using the UNSW-NB15 dataset.  
Figure 18: The confusion matrix for 10 classes in the Inception Time validation with a window size of six using the UNSW-NB15 dataset: normal, fuzzers, generic, exploits, reconnaissance, DoS, analysis, backdoor, worms, and shellcode; all values between 0.50 and 99.  
Table 10 compares the Inception Time models in detecting the initial cyber-attacks: the first using 1D inputs, and the second with a two-dimensional time-series input utilizing the UNSW-NB15 database and size-six sliding window. We observed the superiority of sliding window technology in some types of attacks.  
Table 11 compares the Inception Time models to the previous study in detecting and classifying multiple attacks using the UNSW-NB15 database. The best result was 98.6 % using sliding window technology.