## 4 Experimental Results
### Results Analysis
The deployed cyber-physical system represents a more advanced and intelligent approach to intrusion detection, integrating cutting-edge techniques and optimizing the classification process. It offers improved adaptability, efficiency, and performance compared to traditional IDSs. Figure 11: F1-score analysis with respect to different types of attacks. Figure 12: Comparative analysis with other IDS approaches based on accuracy. Figure 14 compares the conventional PSO-based classification [41] and the proposed EFDPN models using the IS-1 dataset. In order to demonstrate the effectiveness of optimization in the security framework, the optimization-integrated classifier models are compared in this study. For this assessment, standard machine learning algorithms, including RF, DT, KNN, and RC, are considered, which predict intrusions in the dataset according to the features chosen via PSO. In contrast to these algorithms, the proposed EFDPN algorithm yields improved detection results. Since the F3S technique provides the best solution with an increased convergence rate, it effectively reduces the dimensionality of features before the training and testing processes. Therefore, the proposed F3S incorporated with the DPN model greatly outperforms the other classification approaches. A comparative analysis based on the classifier's training time was performed, as shown in Figure 6.0 A good classification model should minimize the training time and offer an increased attack detection rate. Typically, the classifier's training time can be increased with the use of high-dimensionality features, which also increases the computational complexity of classification. Therefore, the input feature set used for the classifier's training must be optimized for better predictions. According to the results, the proposed EFDPN model could effectively reduce the training time with the use of the F3S algorithm. Figure 6.1 Overall comparative analysis with optimization-based intrusion detection approaches. Figure 6.2 Intrusion detection performance analysis with other IDS approaches. Figure 16 compares the accuracy of various machine learning and deep learning techniques [42] used for intrusion detection. In addition, the classifier's detection accuracy is estimated and compared for both IS-1 and IS-2, as shown in Figures 17 and 18, respectively. In addition, a qualitative security analysis [31] was also performed in this study, as shown in Table 3, based on the following parameters: false acceptance rate, accuracy, detection rate, number of features, and time consumption. Overall, the estimated results demonstrate that the proposed EPDN cyber-physical system provides effective prediction results when compared with the existing algorithms due to the inclusion of the F3S and TSO algorithms. This is because effective dimensionality reduction was achieved with the use of the F3S technique and the classifier sigmoid function was computed with the use of the TSO algorithm. Figure 6.3 Comparative analysis based on accuracy. Figure 6.4 Comparative analysis with other IDS approaches based on training time (s). Figure 6.5 Accuracy analysis using IS-1. Figure 6.6 Accuracy analysis using IS-2.