## 7 Conclusions  
In this paper, work was carried out to evaluate the performance of each of the selected DenseNet and Inception Time models for detecting cyber-attacks, as they were tested on three recent datasets (ToN-IoT, Edge-IIoT, and UNSW-NB15). The results were compared based on accuracy, recall, precision, and F1-score. The results showed the superiority of the Inception Time approach in the ToN-IoT database. The highest accuracy that we obtained with all of the features of the DenseNet approach was 99.9% in the Win10-Network model. There was an increase in accuracy when using the Inception Time approach, with a resolution of 100% for the Win10-Network model based on multiple classes. Inception Time is playing an increasingly important role and is becoming a prominent direction for research. Therefore, we focused our work on this model and implemented it on three datasets. Moreover, using the Edge-IIoT database with Inception Time with a rating of 15 classes and 61 features, we obtained the highest result, with an accuracy of 94.94%. As for using the UNSW-NB15 database with Inception Time and 43 features for 10 classes, we achieved \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Type Class**} & \multirow{2}{*}{**Inception Time**} & \multicolumn{6}{c}{**Inception Time-Window**} \\  & & \multicolumn{3}{c}{**Size Six**} & \multicolumn{3}{c}{**Support**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline Analysis & 0.42 & 0.05 & 0.09 & 0.52 & 0.22 & 0.31 & 535 \\ Backdoor & 0.69 & 0.07 & 0.13 & 0.75 & 0.03 & 0.05 & 466 \\ DoS & 0.51 & 0.06 & 0.11 & 0.39 & 0.23 & 0.29 & 3271 \\ Exploits & 0.58 & 0.93 & 0.72 & 0.65 & 0.86 & 0.74 & 8905 \\ Fuzzers & 0.67 & 0.54 & 0.60 & 0.74 & 0.69 & 0.72 & 4849 \\ Generic & 0.99 & 0.98 & 0.99 & 0.99 & 0.98 & 0.09 & 43096 \\ Normal & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 443,753 \\ Reconnaissance & 0.91 & 0.73 & 0.81 & 0.82 & 0.75 & 0.78 & 2798 \\ Shellcode & 0.55 & 0.58 & 0.57 & 0.67 & 0.30 & 0.42 & 302 \\ Worms & 0.80 & 0.11 & 0.20 & 0.00 & 0.00 & 0.00 & 35 \\ Accuracy & 0.984 & & & 0.986 & & & \\ Weighted average & 0.98 & & & 0.98 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification report comparison between Inception Time and inception time with six windows using the UNSW-NB15 dataset. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Research** & **Method** & **Acc** & **Pre** & **Rec** & **F1** & **Class** & **Feature** \\ \hline [PERSON], et al. [19] & Densely-ResNet & 73.93 & 80.94 & 96.68 & 88.11 & Multi & \\ [PERSON], et al. [12] & RF & 95.43 & 0.96 & 0.97 & 0.97 & Multi & 42 \\  & DT & 94.20 & 0.93 & 0.98 & 0.96 & & \\ [PERSON], et al. [36] & ANN & 0.97 & 0.96 & 1.00 & - & Multi & 5 \\  & CNN & 0.96 & 0.95 & 1.00 & - & & \\ [PERSON], et al. [13] & MIP & 84.24 & 83.60 & 84.24 & 82.85 & Multi & 23 \\ [PERSON], et al. [37] & RF-DT & 89 & 0.99 & 0.85 & 0.91 & Multi & 4 \\ [PERSON], et al. [38] & ANN & 75.62 & 79.92 & 75.61 & 76.58 & Multi & 42 \\  & KNN & 70.09 & 75.79 & 70.21 & 72.03 & & \\ Our proposed method & Inception & 98.4 & 99.0 & 97.9 & 98.5 & Multi & 43 \\  & Inception & 98.6 & 98.9 & 98.4 & 98.7 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of the results with previous studies using the UNSW-NB15 dataset. an accuracy of 98.4%. The results improved when using the sliding window approach, reaching an accuracy of 98.6%. This is one of the contributions of this paper. One of the challenges we faced in our experiments was memory, so we used class weights instead of smote, where our experiments were within the limited CPU resources of memory, usage, and test time. Not applicable. The used dataset in this work (UNSW-NB15 and TON_IoT Datasets)is available Online. [[https://research.unsw.edu.au/projects/toniot-datasets](https://research.unsw.edu.au/projects/toniot-datasets)]([https://research.unsw.edu.au/projects/toniot-datasets](https://research.unsw.edu.au/projects/toniot-datasets)) (accessed on 3 April 2022).