# IoT Botnet Attack Detection Model Based on DBO-Catboost
## 5 Conclusions  
To address the issue of designing an effective zombie network detection model and improving detection performance, this paper proposes a zombie network detection model based on the improved DBO-CatBoost. We improved the initial population generation strategy of the dung beetle optimization algorithm, replacing random initialization with the centroid opposition-based learning strategy, and applied it to the field of IoT botnet network detection. The improved DBO-CatBoost model utilized the dung beetle optimizer to optimize the hyperparameters of CatBoost, reducing the cumbersome and repetitive process of hyperparameter tuning and improving efficiency. The proposed method was validated using real-world IoT traffic datasets. Experimental results demonstrate that the proposed approach achieved accuracy rates of 96.10% and 98.57% on the Botnet and Bot-IoT  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **Accuracy** & **F1-Score** \\ \hline Catboost & 94.38\% & 94.25\% & 94.25\% & 94.25\% & 94.25\% \\ BO-GP-DT & 96.73\% & 96.68\% & 96.68\% & 96.68\% \\ BO-LGBM & 97.10\% & 97.06\% & 97.06\% & 97.06\% \\ DBO-Catboost & 98.13\% & 98.12\% & 98.12\% & 98.12\% \\ IDBO-Catboost & 98.62\% & 98.57\% & 98.57\% & 98.57\% \\ GWO-OCSVM & 96.54\% & 96.50\% & 96.50\% & 96.50\% \\ PSO-ONE-SVM & 97.08\% & 97.01\% & 97.01\% & 97.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental Comparison of Various Models on BotIoT.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{6}{c}{**Average Time(s)**} \\ \cline{2-9}  & **IDBO-Catboost** & **DBO-Catboost** & **BO-GP-DT** & **BO-LGBM** & **GWO-OCSVM** & **PSO-ONE-SVM** & **Catboost** \\ \hline Botnet & 38.21 & 39.84 & 46.75 & 45.31 & 47.82 & 46.52 & 51.89 \\ BotIoT & 20.19 & 23.44 & 31.67 & 27.05 & 30.70 & 28.55 & 37.42 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average time.  
datasets, respectively. It outperformed all the compared models in terms of classification performance and exhibited a certain level of generalization capability.  
There are some limitations in this study that need to be addressed. When dealing with imbalanced datasets, further research is needed to improve the focus on the minority class and enhance the detection performance of the model on imbalanced data. Additionally, in the future, introducing interpretability mechanisms will be beneficial to improve the explainability of the model.  
Conceptualization, C.Y.; Writing--original draft, C.Y.; experiment, C.Y.; resources, C.Y.; formal analysis, C.Y. and W.G.; project running, W.G. and Z.F.; supervision, W.G. and Z.F.; funding acquisition, Z.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant no. 11864005).  
The dataset used can be located 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 20 April 2023) and [[https://www.unb.ca/cic/datasets/botnet.html](https://www.unb.ca/cic/datasets/botnet.html)]([https://www.unb.ca/cic/datasets/botnet.html](https://www.unb.ca/cic/datasets/botnet.html)) (accessed on 22 April 2023).  
The authors declare no conflict of interest.