## 4 Results and Discussion
### Performance Evaluation of UNSW-NB15  
The comparison between the existing and proposed methods in terms of precision, accuracy, recall, error, and F1-measure is demonstrated in Figure 4. The proposed method is compared with MOPSO-Levy-KNN and AAFSA with GA-FR-CNN and GWO-PSO-RF. The existing GWO-PSO-RF attained a meager F1-measure value of 79.0%, respectively. However, the model had a low-quality solution and high complexity. The existing MOPSO-Levy-KNN achieved a higher F1-measure value of 87.3%, respectively. As a result, the existing MOPSO-Levy-KNN had storage and memory issues for large datasets. The existing AAFSA with GA-FR-CNN attained a higher recall value of 94.5%. However, the existing model affected the network's convergence rate on the error surface. The existing GWO-PSO-RF attained a high error value of 15.54, respectively. However, the existing GWO-PSO-RF model failed to detect cyber-attacks. Figure 4: Performance metrics for the UNSW-NB15 dataset. The proposed method yields higher results than MOPSO-Levy-KNN, AAFSA with GA-FR-CNN, and GWO-PSO-RF in precision, accuracy, recall, error, and F1-measure. Compared to existing models, such as GWO-PSO-RF and MOPSO-Levy-KNN, the proposed method has higher F1-measure and recall values to detect more accurate and effective cyber-attacks. The study's implications are that the suggested model presents a more effective and efficient solution to protect against cyber threats. Figure 5a,b demonstrates the accuracy and loss curve analysis. The diagram proves that the labeled technique positively impacts convergence. The dataset has been integrated into two phases: training and testing. The researchers created 75% and 25% of the training and testing data, respectively, for this study. The processed training set was used to train the indicated technique for 200 epochs during the exercise. Here, the learning rate was set to 0.1. Figure 6 illustrates the confusion matrix of the proposed Op-ReDMAT method generated on UNSW-NB15. The proposed model detects samples 13,905 as normal, 9939 as generic, and 8407 as exploits, etc. Thus, the proposed model detects the sample correctly and clearly in the confusion matrix. Figure 5: (a,b): Accuracy and loss analysis for the UNSW-NB15 dataset. Figure 6: Confusion matrix proposed dataset. The ROC curve for detecting cyber-attacks is shown in Figure 7. A receiver operating characteristic curve (ROC) is used to graphically represent the performance of a binary classifier model at different threshold circumstances. The true and false favorable rates (TPR-FPR) are calculated at every threshold level to create the ROC curve. The suggested ROC curve reached 97.38, respectively. The TPR and FPR for the UNSW-NB15 dataset are shown in Figure 8a,b. In Figure 8a, the proposed TPR attained 0.9982, respectively. The existing RNN achieved a very low TPR of 0.94, respectively. However, the existing RNN needs more labeled data and space to train. Furthermore, the existing LR attained 0.996, respectively. However, the existing LR had a high computational load and was very expensive. In Figure 8b, the proposed FPR attained 0.028, respectively. The existing SGD attained 0.073, respectively. However, the model had high computational requirements. This analysis shows that the TPR and FPR can perform better in the proposed model. According to the findings displayed in Table 3, the accuracy of the proposed model is the highest at 98.82% compared to other models, including DBN, RNN, LR, and SGD. Even though LR and SGD have a perfect recall and high precision of 98%, it is essential to  
Figure 8: (**a,b**): True and false positive rate for the UNSW-NB15 dataset. Figure 7: Binary classifier model performance. include the accuracy of the proposed model, which was equal to 97.2% and the recall equal to 98.5%, which also demonstrates a high ability to detect a cyber-attack.