## 4 Results and Discussion
### Performance Analysis for CICIDS2017  
Figure 9 compares existing and proposed precision, accuracy, F1-score, and recall methods. The DNN, LSTM, and CNN are compared with the proposed technique. The existing DNN attained higher accuracy values of 94.6%, respectively. However, the existing DNN needs more interpretability issues. The existing LSTM achieved a low F1-score value of 93.5%, respectively. However, the existing LSTM needs more training data. Furthermore, the existing CNN had an average value of 96.9%. As a result, the existing CNN needs high computational requirements. Compared to the existing methods, the proposed technique attained higher values. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** \\ \hline DBN & 92.3\% & 0.00\% & 0.00\% \\ \hline RNN & 95.3\% & 0.00\% & 0.00\% \\ \hline LR & 98.17\% & 98\% & 1.00 \\ \hline SGD & 97.9\% & 98\% & 1.00 \\ \hline Proposed & 98.82\  
The observation in Figure 9 also validates the importance of the proposed technique's improvement over the existing models like DNN, LSTM, and CNN by highlighting the difference in accuracy, precision, F1-score, and recall. However, DNN has some limitations; while it has high accuracy, it could be improved in the interpretability of results. LSTM needs a large amount of training data, and CNN requires more computational power. The proposed method obtains better performance for these metric values and overcomes the above shortcomings, proving that the proposed method has good application prospects for efficient time series data processing. In conclusion, the proposed technique offers a better and more optimal solution than the existing methods in terms of improved performance and reduced chances of failure. Figure 10a,b illustrates the accuracy and loss curve analysis. The accuracy values gradually increase to 50 epochs, so the model attains high accuracy. When calculating the loss function, the testing loss is high, from 0.1 after 50 epochs, and the training loss is below 0. Thus, the proposed model consumes less time for training and testing. The results presented in Figure 10 demonstrate the importance of the learning effectiveness of the proposed model. The constant rise in the level of accuracy up to fifty epochs shows that the model is learning patterns and thus attains its maximum accuracy. The fact that the training loss is low, and the testing loss starts to level off around the 50 th epoch, indicates that the model is learning well, tested on rounds, and does not overfit. Consequently, these findings suggest that the proposed model is promising for practical implementation in terms of accuracy and time to accomplish training and testing phases. Figure 11 illustrates the confusion matrix of the proposed Op-ReDMAT method generated on CICIDS 2017. The proposed model predicts 97 regular attacks and 733,593 cyber-attacks from the total number of samples. The confusion matrix depicted in Figure 11 confirms the capability of the proposed Op-ReDMAT method in distinguishing between the standard and cyber-attack instances using the CICIDS 2017 dataset. This means that the model is efficient in detecting 97 standard samples and 3593 cyber-attack samples, suggesting efficiency in detecting malicious activity. These figures demonstrate that the high number of correctly identified cyber-attacks stems from the accuracy and efficacy of the model in differentiating between regular traffic and malicious behaviors. Finally, the conclusions prove that the Op-ReDMAT method is highly effective in accurately detecting cyber-attacks, which shows the effectiveness and potential of the technique as a tool for improving the reliability of cybersecurity systems. Figure 10: **(a,b)**: Accuracy and loss analysis for the CICIDS2017 dataset. Figure 12 demonstrates the ROC value calculated with an accurate and false positive rate. The proposed ROC achieves a high accuracy value of 98.5.