# IoT Botnet Attack Detection Model Based on DBO-Catboost
## 4 Experimental Results
### Experimental Results

Table 4: Comparison of experimental results. Figure 2: Changes in AUC values for the number of iterations. (**a**) IDBO-Catboost, (**b**) DBO-Catboost, (**c**) PSO-Catboost, and (**d**) GWO-Catboost. indicating that IDBO-Catboost achieved a better balance between sensitivity and specificity. Additionally, the AUC value of IDBO-Catboost was the highest among all the comparison models, reaching 0.99. Therefore, this result effectively demonstrates that the model has good performance and high discriminative ability at different thresholds. After validating the optimization effect of IDBO on the Catboost algorithm, we compared it with the recently proposed models, namely BO-GP-DT (2020), GWO-OCSVM (2020), PSO-ONE-SVM (2021), BO-LGBM (2022), and the original DBO-Catboost. The parameter settings for the models are shown in Table 5, and the experimental results from the Botnet dataset are presented in Table 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **Accuracy** & **F1-Score** \\ \hline BO-GP-DT & 95.19\% & 95.06\% & 95.06\% & 95.06\% \\ BO-LGBM & 95.53\% & 95.47\% & 95.47\% & 95.47\% \\ DBO-Catboost & 95.84\% & 95.75\% & 95.75\% & 95.75\% \\ IDBO-Catboost & 96.21\% & 96.10\% & 96.10\% & 96.10\% \\ GWO-OCSVM & 95.45\% & 95.38\% & 95.38\% & 95.38\% \\ PSO-ONE-SVM & 95.62\% & 95.55\% & 95.55\% & 95.55\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental Comparison of Various Models on Botnet. Figure 3: AUC values and ROC curves of different models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Parameter Settings** \\ \hline BO-GP-DT & max\_depth = 6, max\_leaf\_nodes = 11, min\_samples\_leaf = 2, min\_samples\_split = 7 \\ BO-LGBM & max\_depth = 18, learning\_rate = 0.58, n\_estimators = 800, \\ DBO-Catboost & Iterations = 849, learning\_rate = 0.12, depth = 8 \\ IDBO-Catboost & Iterations = 816, learning\_rate = 0.17, depth = 7 \\ GWO-OCSVM & \(\
u\) = 0.061, \(Y\) = 0.21, Kenel = rbf \\ PSO-ONE-SVM & \(\
u\) = 0.053, \(Y\) = 0.22, Kenel = rbf \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameter settings. From the results in Table 6, it can be observed that IDBO-Catboost achieved the highest accuracy of 96.21% among all the comparative models. It outperformed BO-GP-DT, BO-LGBM, GWO-OCSVM, PSO-ONE-SVM, and the original DBO-Catboost by 1.04%, 0.63%, 0.72%, 0.55%, and 0.35%, respectively. Similarly, IDBO-Catboost also demonstrated superior precision, recall, and F1 score compared to other models, indicating its optimal classification performance. There are two main reasons for these results: firstly, compared to decision trees and SVM algorithms, gradient-boosting tree-based algorithms such as Catboost and LGBM perform better when dealing with large-scale datasets, and the Catboost algorithm is particularly effective in handling noisy data in the dataset. Secondly, for optimization algorithms, the ability to balance global and local searches is crucial. In the dung beetle optimizer, the breeding behavior ensures that new individuals have better fitness, foraging behavior accelerates the speed of local search, stealing behavior utilizes better solutions discovered in the global search, and rolling behavior increases the algorithm's diversity, helping it to escape local optima and further improving the global search capability. Finally, the improved DBO algorithm generates an initial population that is closer to the optimal solution, allowing it to better find the optimal parameter combination. Through the combined effect of these behaviors, the algorithm achieves a balance between global and local search, thereby enhancing its search ability and optimization performance, effectively improving the detection and classification performance of the model. To evaluate the generalization ability of the proposed method, the Bot-IoT dataset [37] was used for performance assessment.