# A GNN-Based False Data Detection Scheme for Smart Grids
## 5 Performance Evaluation
### Experimental Settings  
This section verified the performance evaluation of our raised methods via simulation experiments. The several methods compared with our scheme were DTC, SVM-TS, RvNN, BiGCN, and TGNF. Among them, DTC and SVM-TS were machine learning-based detection methods, RvNN was a deep learning-based detection method, and BiGCN and TGNF were graph neural network-based detection methods. **DTC**[30]: This method designed statistical features and used a decision tree model for anomaly categorization. **SVM-TS**[31]: This method extracted text features of false and distorted data, user features involved in transmitting data, and temporal information and features during the transmission process, and used a linear support vector machine model to determine the authenticity of the data. **RvNN**[32]: This method constructs the data transmission process into a bidirectional propagation tree and learns the structural information in the bidirectional propagation tree through a recursive neural network, using it as the basis for classifying abnormal data. **BiGCN**[33]: This method used graph convolutional networks to separately study the top-down transmission graph and bottom-up diffusion graph formed during data transmission, to obtain the diffusion and propagation characteristics of events, and aggregated the two to capture the ultimate event representation for abnormal data classification tasks. **TGNF**[34]: This method modeled the event propagation process as a dynamic graph, aggregated data structure information at different times through an attention mechanism, and used adversarial learning to focus on the differences between structural information. Early anomaly detection aimed to accurately determine the authenticity of data transmission in the early stages, which required the method to extract key clues that could distinguish between false and distorted data from a small amount of information. This was another important indicator for measuring the effectiveness of the method. Figure 4 is the result of early anomaly detection in IEEE 118-bus. We could see that, compared to other methods, the STGNN achieved better performance in various periods of early data transmission. With increasing data transmission time, the accuracy of anomaly detection of various methods was improved to varying degrees. This was because, in the early stage of data transmission, the relevant information was relatively scarce, resulting in poor classification performance of the methods. With the transmission and diffusion of data, the available information related to it continued to increase, enabling the method to learn more key features, so the accuracy would continue to rise. Deep learning-based anomaly detection methods exhibited superior classification performance at the beginning of transmission and could achieve high classification accuracy even in situations where available information was scarce. This indicated that, compared to manually designed features, deep learning-based methods could extract clues from limited information that were beneficial for identifying the authenticity of events. The data in the data set were divided into normal data (N), false data (F), correct data (C), and distorted data (D). According to Tables 1 and 2, it can be found that, compared to machine learning-based anomaly detection methods, deep learning-based anomaly detection methods have achieved better results in various evaluation metrics. This is mainly because the efficacy of machine learning-based methods is highly dependent on the quality of feature engineering, that is, whether the various features carefully designed by humans are discriminable. This task relies heavily on the professional knowledge of researchers and can only obtain relatively superficial features. Deep learning methods can automatically learn high-level hidden representations in samples, thereby generating more effective representation vectors. Therefore, utilizing deep learning methods to identify anomalies in smart grid networks plays an important role.