## 4 Results and Discussion
### Discussion  
Different studies have employed multiple techniques for intrusion detection systems [16; 17; 18; 19; 20; 21]. However, these models have a very high computational load, are slower on large datasets, need better data generalization, and are highly complex. An enhanced optimized residual dense-assisted multi-attention transformer (Op-ReDMAT) model was introduced to overcome these issues. Here, the UNSW-NB15 dataset attained higher precision, accuracy, F1-score, error rate, and recall of 97.2%, 98.82%, 97.8%, 2.58, and 98.5%, respectively. Moreover, the CICIDS2017 dataset attained higher precision, accuracy, F1-score, and recall of 98.6%, 99.12%, 98.8%, and 98.2%, respectively. From Table 5, it can be seen that the proposed model performed better than the existing methods.  
Classifier fusion is the procedure of using the outputs of multiple classifiers to boost the general decision-making process and has, in the recent past, attracted much attention in the improvement of the stability and accuracy of IDS. [PERSON] et al. [22] showed that by fusing different classifiers, it is possible to improve overall accuracy through using fusion methods such as using majority vote or weighted average. This approach is particularly suitable for the application in the proposed Op-ReDMAT model, since the integration of the classifier fusion can improve the detection rate and the variance against a broad spectrum of cyber threats. The addition of classifier fusion into the Op-ReDMAT can result in better resistance to intrusion detection, particularly with diverse and large sets of samples such as UNSW-NB15 and CICIDS2017. Future works may try to implement the classifier fusion strategies with the proposed model to enhance the reliability in identifying attacks and to make the IDS more accurate in a variety of attack cases, which makes it even more actionable in the real life.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Methods** & **Precision** & **Accuracy** & **F1-Score** & **Recall** \\ \hline LSTM and AE [16] & 0.00\% & 99.0\% & 0.00\% & 0.00\% \\ \hline Two different ML models combined with an extra tree classifier [17] & 0.00\% & 96.2\% & 0.00\% & 0.00\% \\ \hline A hybrid attention mechanism combined with an enhanced algorithm [19] & 95.03\% & 96.3\% & 95.0\% & 95.19\% \\ \hline Proposed Model & 98.6\% & 99.12\% & 98.8\% & 98.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the existing methods.  
A significant limitation of this study is the lack of rigorous statistical analysis of the results to the extent that confidence intervals or \(p\)-values were not used. It is, therefore, important to supplement them with factors such as \(p\)-values or confidence intervals to evaluate the robustness and significance of the results. Further, though the performance of the proposed model is promising, the study might require a more adequate analysis of the number of computational resources needed to implement the model on a larger scale. This would give a broader approach to its usage in different real-life situations.  
Future studies should use more scientific measures, such as \(p\)-values and confidence intervals, to offer more reliable outcomes. Enlarging the scope of the study to incorporate a wider range of datasets and using real-time data to test the model would further confirm the model's usability in other settings. Moreover, an analysis of the computational resource's utilization and assessment of the model in the different topologies of networks can enrich the applicability and efficiency of the proposed approach against cybersecurity issues.