## 4 Experimental Results
### Results Analysis  
This section validates the performance and results of the proposed EEDPN cyber-physical system using a variety of measures and public datasets. The sample network environment created in this analysis is shown in Figure 5, where the green-colored nodes are considered normal, and other colors indicate the different types of intrusions. By using the combination of F\({}^{3}\)S + DPN + ISO models, intrusions can be accurately predicted in the proposed framework. For the testing and validation of this system, emerging benchmarking datasets such as UNSW-NB 15 (Intrusion dataset IS-1) and NSL-KDD (Intrusion dataset IS-2) were considered. Figure 4: Performance analysis with and without optimization algorithms for IS-1. Similarly, the results were estimated for IS-2, as shown in Figure 6. This analysis was mainly carried out to determine the importance of using the F3S mechanism in the intrusion detection approach. The results reveal that performance was greatly improved with the use of the F3S algorithm for both datasets. As the increased dimensionality of features may affect the performance of a classifier with low accuracy, it is essential to squeeze the feature dimensionality for improved detection results. Figures 7-10 show the results of the validation and comparison of the accuracy, precision, recall, and f1-score parameters for both conventional [39] and the proposed intrusion detection approaches with respect to the different types of attacks. These results include the following attack categories: normal attack, brute force attack, botnet attack, and web attack. A subset of the model's performance was used to evaluate the accuracy of the algorithm. One of the metrics used to evaluate the classification models was accuracy, as computed in Equation (22). Precision implies a high rate of accurate estimation. It is a percentage of all genuine positives that the model claims are connected with all positives that the model expects, and it is estimated using Equation (23). Recall is also referred to as the true positive rate (as computed in Equation (24)), which compares the total positives in all the system states to the actual total of positives in the data. Additionally, model performance can be estimated using the F1 score, which is the weighted average of precision and recall, as computed in Equation (25). The obtained results reveal that the proposed EPDPN-based security model provides an effective attack detection result when compared to the other techniques. Due to the inclusion of the TSO and F3O algorithms, the security performance results are greatly enhanced in the proposed cyber-physical system. Figure 5: Network deployment. Figure 6: Performance analysis with and without optimization algorithms for IS-2. Figure 8: Comparative analysis with other IDS approaches for Botnet attacks. Figure 7: Comparative analysis with other IDS approaches for normal attacks. Figure 11 validates the F1-score values of all the existing intrusion detection approaches in accordance with the different types of attacks. The estimated results indicate that the proposed EFDPN model triumphs over the conventional DBN-, SVM-, RNN-, SNN-, and FNN-based intrusion detection approaches, presenting an increased F1-score value. The F3S algorithm plays a vital role in obtaining better prediction results in the proposed framework since it eliminates irrelevant features in order to improve the prediction process of the classifier. Figure 10: Comparative analysis with other IDS approaches for web attacks. Figure 9: Comparative analysis with other IDS approaches for brute-force attacks. Figures 12 and 13 show the results of the validation and comparison of the existing [40] and proposed security methodologies performed using the IS-1 dataset. The obtained results also indicate that the proposed EFDPN model outperforms the other models, yielding high performance results.