## 4 Results and Discussion
### Performance Analysis
#### 4.5.2 Analysis in Terms of Different Evaluation Metrics  
When assessing the classification ability of an IDS, the various assessment metrics such as accuracy, precision, recall, kappa, FPR, and f-measure offer a thorough understanding of the model's efficacy for differentiating between attack and normal instances. The performance of the IDS can be understood, and well-informed decisions about its deployment and refining can be made to improve network security by comprehensively examining these evaluation metrics. Precision is considered one of the significant metrics since it provides information about the model's ability to prevent false alarms by determining the percentage of accurately recognized attacks among all samples chosen as attacks. Figure 7 presents the graphical representation of precision results for proposed and existing methods. The graph shows that the proposed approach is more effective in classification than the existing methods. The overall precision value of the proposed approach is 99.4%, whereas the precision values of RF, CNN, GRU, and DQN are 97.2%, 97.4%, 97.6%, and 98.2%, respectively. Figure 6: Confusion matrix of the proposed work. Figure 7: Precision comparison of the proposed and existing works. Figure 8 presents a comparison of the graphical recall of the proposed and existing works. The graph shows that the proposed approach yielded a higher recall value than other works. The proposed approach utilized better and more crucial representations using DAE to approximate the Q-function and optimal policy selection for better classification. The DAE model effectively represents features without causing any loss of information. This specific advantage of the model supported the SVM classifier to produce stable and effective outcomes. The overall recall value of the proposed approach is 99.1%, and the recall values scored by RF, CNN, GRU, and DQN are 97.3%, 97.5%, 97.6%, and 98.3%. The f1-score comparison of proposed and existing works is graphically plotted in Figure 9. The figure shows that the proposed approach is more optimal and results in a better f-measure value than other methods. Since the precision and recall values of the proposed method are high, the f-measure of the proposed method is also high, assuring better classification. The overall f-measure value of the proposed method is 99.3%, whereas the f-measure values of RF, CNN, GRU, and DQN are 97.1%, 97.2%, 97.4%, and 98.1%, considerably. Accuracy is considered a major parameter for determining the accurate classification of normal and attack samples. Figure 10 presents the accuracy comparison of proposed and existing works regarding attack classification. The comparison shows that the proposed method's accuracy is high compared to other works. A significant reason for high  
Figure 8: Recall the comparison of the proposed and existing works. Figure 9: F1-score comparison of the proposed and existing works. accuracy is the use of a DAE model and IFSA to enhance the RF and improve classification performance. The IFSA's optimal policy selection effectively supports the SVM classifier to provide better classification results. Among the compared methods, the EDRL model resulted in a better accuracy score, and the RF classifier resulted in a low accuracy score. The overall accuracy value attained by the proposed model is 99.6%, whereas the accuracy values attained by RF, CNN, GRU, and DQN are 97.4%, 97.7%, 97.8%, and 98.5%. The FPR comparison of the proposed and existing works is presented in Figure 11. The figure shows that the FPR of the proposed work is comparatively lower than the other works. This indicates that the misclassifications produced by the proposed method are very low, resulting in effective classification. Among the compared methods, the value of FPR for the RF model is lower than the other three models. The overall FPR value of the proposed approach is 0.003%, whereas the FPR values of the compared methods RF, CNN, GRU, and DQN are 0.028%, 0.026%, 0.021%, and 0.015%, respectively.