## 5 Conclusions  
The rapid development of IoT technology has brought convenient services and applications. However, the ensuing IoT attacks are increasing rapidly. Therefore, this article proposes a classification model that combines MLP and GANs to provide an appropriate data amplification mechanism to improve the classification performance of a single class. The experimental results demonstrate that when using the BoT-IoT dataset, the proposed method effectively augments data for the \"Theft\" and \"Normal\" classes, resulting in classification accuracy, precision, recall, and F1-score, all exceeding 90%. The overall classification performance in terms of accuracy, precision, recall, and F1-score consistently surpasses 95%. Furthermore, to validate the robustness and practicality of the proposed method, it was applied to different datasets, and the results were observed. When using the ToN-IoT dataset, the proposed method indeed augments data for the \"DoS\", \"DDoS\", \"Mitm\", and \"Password\" classes, leading to classification accuracy, precision, recall, and F1-scores all exceeding 90% for these classes. The overall classification performance, in this case, consistently exceeds 97%.  
In future work, we will explore various types of datasets and focus on the quantity and timing of precise data augmentation to speed up the training time and testing time of the classification model. As the dataset currently used is in CSV format, unlike real-world IoT attack data, feature selection for the training set will be explored. Thus, the proposed method can be directly applied to actual network equipment and achieve instant and accurate attack classification.  
Conceptualization, H.-C.C. and Y.-J.L.; Methodology, H.-C.C.; Software, Y.-J.L.; Validation, H.-C.C. and Y.-J.L.; Formal analysis, H.-C.C. and Y.-J.L.; Investigation, H.-C.C. and Y.-J.L.; Data curation, Y.-J.L.; Writing-original draft, H.-C.C. and Y.-J.L.; Writing-review & editing, H.-C.C.; Visualization, H.-C.C. and Y.-J.L.; Supervision, H.-C.C.;  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Dataset** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Remark** \\ \hline GANs + AE [20] & BoT-IoT & 97.11\% & 99.33\% & 97.33\% & 98.31\% & Binary classification \\ CGANs + FNN [21] & BoT-IoT & 77.01\% & 77.74\% & 77.00\% & 76.56\% & Average of multivariate classification \\ Proposed & BoT-IoT & 98.83\% & 97.62\% & 95.30\% & 96.42\% & Average of multivariate classification \\ DNN [8] & ToN-IoT & 69.53\% & 56.84\% & 69.53\% & 61.96\% & Average of multivariate classification \\ Proposed & ToN-IoT & 98.53\% & 97.99\% & 97.99\% & 97.36\% & Average of multivariate classification \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison of the classification performance between the proposed method and other methods.  
Project administration, H.-C.C.; Funding acquisition, H.-C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Endian Hardware Co., Ltd., Taiwan, under grants 111B515 and 111B516.  
Not applicable.  
The data presented in this study are openly available in BoT-IoT at [[https://research.unsw.edu.au/projects/bot-iot-dataset](https://research.unsw.edu.au/projects/bot-iot-dataset)]([https://research.unsw.edu.au/projects/bot-iot-dataset](https://research.unsw.edu.au/projects/bot-iot-dataset)) (accessed on 15 April 2023) and ToN-IoT at [[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 15 April 2023).  
The authors declare that this study received funding from the Endian Hardware Co., Ltd. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.