## 4 Analysis of Experimental Results
### Comparison of Experimental Results  
The Support Vector Machine (SVM) model [22] was compared with the GAN method presented in this paper. A total of 2000 abnormal samples (where data for a random sample varies within interval \(\pm[30\%,50\%]\)) and 3000 normal samples were generated using VISSIM. Precision, recall, and F1 score are commonly used metrics to evaluate the performance of an anomaly detection model.  
Precision refers to the probability that a sample predicted as correct is actually correct, and it differs from the concept of accuracy. It mainly indicates the degree of correctness in the results of positive samples. Recall, on the other hand, refers to the probability that a sample is predicted as positive among those that are actually positive, primarily reflecting the extent of prediction for positive samples. The F1 score considers both precision and recall, aiming to maximize both simultaneously. The specific experimental results are shown in Table 5.  
Through comparative experiments, it was found that the classical SVM can effectively identify all positive sample data during the judgment process, with a precision higher than that of the method proposed in this paper. However, classical SVM tends to misclassify normal samples as anomalies quite frequently, which is precisely the focus of this paper. This is because, when a signaling system is attacked, hackers can generate some anomalous data that disrupts the normal operation of signals and control systems. Therefore, we need to accurately identify all anomalous data; thus, the model design places emphasis on the authenticity of the identification results themselves. The algorithm presented in this paper exhibits a higher recall rate. Ultimately, the F1 score of this method surpasses that of classical SVM, reflecting the algorithm's consideration of the overall relevance of the problem. Its primary goal is to quickly identify anomalous data to assist the signal  
Figure 5: ROC curve.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Norm** & **SVM/\%** & **GAN Model/\%** \\ \hline precision & 100 & 86.7 \\ recall & 78.4 & 95.7 \\ F1 score & 87.9 & 90.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of Evaluation Indicators.  
control system in restoring normalcy. Therefore, the detection performance of the proposed method is superior to that of the SVM algorithm.  
GAN model's discriminator can be used to determine if there are any anomalies in traffic information. If anomalies are detected, the generator of the GAN model can be used to restore the data, allowing for a comparison with the anomalous data to identify the specific locations of the anomalies. The algorithm's accuracy and restoration effectiveness were validated using 1000 anomalous samples, as shown in Table 6.  
Those interested in using this method can select traffic flow data and signal control data from a target area to train a model on a dataset. Then, they can utilize a simulation environment to randomly generate anomalous data. Through our validation, we found that the accuracy in identifying anomalous data can reach over 97.6%. The deviation in repairing anomalous data using the GAN model's generation network can be kept within 13.9%, making it superior compared to similar methods.