## 4 Experiment Results and Discussion  
The datasets (KDD99, CSE-CIC-DS 2018, and UNSW-NB 15) used in this study were selected based on a variety of factors, including the number of samples, attributes, and classifications. TPs (true positives), FPs (false positives), TNs (true negatives), and FNs (false negatives) were all measured to calculate various performance measures. The first performance measure was precision, which is expressed as the percentage of applicable instances found. Recall, characterized as the percentage of relevant instances, was the second performance metric. Despite their often conflicting nature, the ratings of precision and recall are both critical to how effective a prediction strategy is. Therefore, these two measures can be added together and weighted equally to create the F-measure, a single metric. The accuracy measure, which was the final performance requirement, was defined as the percentage of events which were accurately predicted. The proposed OGBTDS-IDS was evaluated using accuracies, precisions, recalls, and F-scores, and the results were compared using other traditional MLTs. Evaluating metrics for precisions (Equation (7)), recall (Equation (8)) F-measures (Equation (9)), and accuracy (Equation (10)) are given below. \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100 \tag{7}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100 \tag{8}\]  
\[\text{F}_{\text{measure}}=2*\left(\frac{\text{precision}*\text{recall}}{\text {precision}+\text{recall}}\right) \tag{9}\]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}}\times 100 \tag{10}\]  
where TP is true positive, FP is false positive, TN is true negative, and FN is false negative. Figure 4 compares the precision rates for the suggested and existing methods for identifying cyberattacks. The outcomes indicate that extracting the desired data can be accomplished by ranking the features using OGBDT. The number of usable features in the proposed model has little to no impact on how well the jointly learned feature transformation performs. Due to the lack of high-dimensional features or derived factors, an OGBDT may identify a comparatively better-sorted collection of inputs in a specified amount of time. The performance of the model was superior to all others, and KDD99 had a higher detection rate than the other two datasets. Recall rate comparison is shown in Figure 5 for proposed and existing models. An increased feature number maximizes recall as well. The OGBDTs achieve higher recall compared to DT, SVM, ANN, and KNN. This is because the EABOs save the computation time of the derived factors, which allows easier fine-tuning of the GBDT. Therefore, the proposed network can be safely used for intrusion detection. Figure 4: Precision rate comparison. The f-measure for the number of features in the given databases for the proposed and current models is shown in Figure 6. The OGBDTs have a high f-measure compared to other models. EABOs use the parameters set with random values, and terminate when predefined stop conditions, such as maximum time, number of parameters, or performance target, are met. This avoids overfitting of the data, which is possible in real-time problems and leads to better performance. Based on the number of features in the given databases, Figure 7 illustrates the accuracy of the proposed and existing models. As a result of OGBDTs, the processing time is reduced and accuracy is increased. The OGBDTs achieve higher accuracy compared to all other models because they do not require a large amount of derived factors during preprocessing. The proposed OGBDTs framework can significantly improve the performance of cyber-attack classification by jointly optimizing the GBDTs and improving the classification results. It can be observed from Figure 8 that the OGBDTs tree outperforms other models by taking the least time to train the framework compared to others.