## 5 Experiments and Results
### Performance of the Proposed Anomaly Detection Method  
The results depicted in Figures 3-5, which were obtained by simulating the proposed network anomaly detection model with a weight parameter value of \(\alpha=0.5\), demonstrate the convergence behavior of the algorithm. It can be observed that, over time, the algorithm gradually converges towards a stable state. The convergence process is characterized by a decreasing trend in the loss function, as shown in Figures 3-5. In intelligent manufacture, convergence is slightly less effective due to a slight lack of data volume. Remarkably, the algorithm exhibits an impressive convergence speed, achieving convergence in less than \(10\) epochs for intelligent manufacturing and Industrial Internet. This rapid convergence is a notable advantage of the proposed network anomaly detection model. By converging quickly, the algorithm is able to efficiently and effectively detect and identify network anomalies. This attribute is particularly valuable in real-time network monitoring scenarios where prompt anomaly detection is crucial for maintaining network security and stability. Figure 3: The loss of network anomaly detection algorithm in the Internet of Vehicles. Furthermore, we scrutinize the performance of anomaly detection based on precision, recall, and F1-score using network data derived from three distinct 5G vertical applications. A critical factor in this analysis is the role of the weight parameter in the calculation of the anomaly score. To fully understand its impact, we initially fine-tune this parameter within our proposed anomaly detection method, taking into consideration the distribution of network data across different application scenarios. Figure 4: The loss of network anomaly detection algorithm in intelligent manufacturing. Figure 5: The loss of network anomaly detection algorithm in the Industrial Internet. Table 2 provides a comprehensive summary of the accuracy of anomaly detection in network data across different small regions within three distinct application scenarios: Internet of Vehicles, intelligent manufacturing, and Industrial Internet. The table presents the results obtained using various weight parameter settings. The findings indicate that anomaly detection using network data from these three application scenarios consistently achieves a high F1-score. This suggests that the proposed anomaly detection algorithms effectively identify and classify anomalies in diverse network environments. Moreover, the average F1-score across all scenarios indicates a generally high level of accuracy in the detection of network anomalies. Furthermore, the consistent performance across the three application scenarios suggests that the proposed approach is robust and adaptable. The ability to achieve accurate anomaly detection in various IoT contexts is vital for maintaining network security and integrity. These results contribute to the advancement of anomaly detection techniques and provide valuable insights for network administrators and researchers in the field of IoT security. In order to provide a more comprehensive evaluation of the proposed anomaly detection method, we compare its performance against a baseline model, i.e., autoencoder-only method. This baseline model serve as a standard measure to evaluate the accuracy of detection using the F1-score as the metric. For the purposes of this comparison, we set the weight parameter \(\alpha\) used in the calculation of the anomaly score in our proposed method to be 0.5. Table 2 visually presents the performance of anomaly detection across three distinct application scenarios: Internet of Vehicles, intelligent manufacturing, and Industrial Internet. Here, our proposed anomaly detection approach is contrasted with the autoencoder-only and adversarial-only methods. The data displayed in Table 2 lead us to conclude that, irrespective of the application scenario, our proposed network anomaly detection method consistently outperforms the autoencoder-only method in terms of detection accuracy.