## 6 Simulation Results  
The suggested Cb-C was implemented in Python on an Intel(R) Core(TM) i7 processor running Microsoft Windows 11 with a 3.40 GHz central processing unit and 16 GB of random access memory under the Google Collaboratory environment. Table 3 lists the hyperparameters and the values selected for the proposed classifier. The proposed classifier was executed with a learning rate of 0.01, 0.05, 0.1, and 0.5 with 100 iterations. Table 3 provides the values obtained by the proposed classifier on 20% of the testing data. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Input Values** \\ \hline \hline \multicolumn{3}{c}{Defining search space for hyper parameters} \\ \multicolumn{3}{c}{iterations} \\ \multicolumn{3}{c}{depth} \\ \multicolumn{3}{c}{learning rate} \\ \multicolumn{3}{c}{optimizer} \\ \multicolumn{3}{c}{best\_params} \\ \multicolumn{3}{c}{model=} \\ \multicolumn{3}{c}{Catboost-Classifier(best\_params)} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameters of the proposed classifier. Table 4 shows that the proposed classifier achieved an average TP rate of 0.99, recall, precision, f1 measure, and ROC, with an FP rate of 0.01. Table 5 shows that out of 74,932 instances, 74,676 were correctly classified and 256 were incorrectly classified. Therefore, the accuracy is 99.66% and the misclassification cost is 0.34. From Table 6, it is inferred that MAE is 0.0025 and RMSE is 0.034. The kappa statistics value is 0.98, which is very close to 1. This shows that the proposed Cb-C tuned by the LOA fits best in predicting more samples as true positives, which produces better accuracy than the other reported techniques. Table 7 shows the comparative analysis of the performance metrics obtained by the proposed classifier with the other reported models in this article. The accuracy obtained by LOA-Cb-C is 99.66%, which is 0.06% less than Gboost, 0.16% less than MVFS, 0.46% less than DLDM, 0.87% less than CNN, 0.87% less than SVM, 1.66% less than RF, 2.01% less than MLML, 2.62% less than DNN, 2.8% less than CNN+RNN, 3.18% less than RNN, 5.06% less than NB, 6.26% less than LR, 6.66% less than NNC-PSOR, 7.7% less than ANN, and 15.86% less than KNN. The error obtained by LOA-Cb-C is 0.34, which is 0.06 times less than Gboost, 0.16 times less than MVFS, 0.46 times less than DLDM, 0.87 times less than CNN, 0.94 times less than SVM, 1.66 times less than RF, 2.01 times less than MLML, 2.62 times less than DNN, 2.8 times less than CNN+RNN, 3.18 times less than RNN, 5.06 times less than NB, 6.26 times less than LR, 7.0 times less than NNC-PSOR, 7.7 times less than ANN, and 15.86 times less than KNN. The value of precision, recall and F1-score obtained by proposed classifier is 0.99, but the value obtained by ANN is 1.00. \begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Output** \\ \hline Accuracy & 99.66 \\ Error & 0.34 \\ Kappa Statistic & 0.98 \\ MAE (Mean Absolute Error) & 0.0025 \\ RMSE (Root Mean Squared Error) & 0.034 \\ Total number of instances & 74,932 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Output of the proposed LOA-Cb-C. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F1\_Measure** & **ROC** \\ \hline Normal & 0.99 & 0.02 & 0.99 & 0.99 & 0.99 & 1.00 \\ Flooding & 0.97 & 0.00 & 0.94 & 0.97 & 0.96 & 0.99 \\ TDMA & 0.93 & 0.00 & 1.00 & 0.93 & 0.96 & 0.95 \\ Grayhole & 0.97 & 0.00 & 0.98 & 0.97 & 0.98 & 1.00 \\ Blackhole & 0.99 & 0.00 & 0.98 & 0.99 & 0.98 & 1.00 \\ Weighted Average & 0.99 & 0.01 & 0.99 & 0.99 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of proposed classifier on test data.