## 4 Experimental Method
### Experimental Results  
Comparing Cert GCN with Text GCN, the difference is that Cert GCN constructs graph certificate datasets using the attributes of the certificates and the names of the certificate documents as nodes, whereas Text GCN processes certificate datasets by constructing the corpus using the words in the certificate file and the certificate documents as nodes. They also use different activation functions and have different learning rates. Figures 4 and 5 depict the accuracy of the training and validation sets of Cert GCN and Text GCN [10], respectively, applied to malicious and benign certificates. As the line graph shows, Cert GCN is 8% more accurate than Text GCN at the point at which the training set has the highest accuracy. Cert GCN is 4% more accurate than Text GCN at the point of highest validation set accuracy. In Cert GCN, the accuracy of the test set is close to 99%, the accuracy of the validation set is close to 95%, and the model is more stable. In Text GCN, the highest accuracy is 92% in the test set and 91% in the validation set, but the model is not yet stable, and there is an upward trend in the accuracy of the model. The fitting speed of Text GCN is slower than that of Cert GCN and the accuracy is not as high as that of Cert GCN. Figures 6 and 7 show the visualization of certificate node classification after training the model. Figure 4: Classification accuracy of Cert GCN model on the training set and validation set of certificates. Figure 5: Classification accuracy of Text GCN model on the training set and validation set of certificates. Tables 3 and 4 compare the evaluation of Cert GCN and Text GCN for detecting malicious and benign certificates. Figure 8 depicts the accuracy, recall, and F1 values of Cert GCN and Text GCN for malicious certificate classification. The diagram shows Cert GCN is 3.86% more accurate than Text GCN, and Cert GCN has 16.05% higher recall and 13.05% higher F1 than Text GCN. Figure 9 depicts the results of the two algorithms for benign certificate detection. The figure shows Cert GCN is 3.42% more accurate than Text GCN, and Cert GCN has 0.46% higher recall and 2.085% higher F1 than Text GCN. Cert GCN achieves good classification results. Figure 6: Visual classification of certificate documents by Cert GCN, in which yellow points are malicious certificate nodes and blue points are benign certificate nodes. Figure 7: Visual classification of certificate attributes by Cert GCN. Different colors in the figure represent different documents, and each point represents an attribute of the document. There are 8061 digital certificate files in total. In this paper, the accuracy of Cert GCN is compared with that of SVM, KNN, MLP, LSTM, NB, and Text GCN. We find that the accuracy of Cert GCN is better than that of all these other algorithms, and Cert GCN achieves 97.41% accuracy in detecting malicious certificates. The model with the lowest prediction accuracy is NB, which has an accuracy of 85.42%, as shown in Table 5 and Figure 10. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **F1** \\ \hline Cert GCN & 92.89\% & 99.53\% & 96.10\% \\ Text GCN & 89.47\% & 99.07\% & 94.02\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of model results of benign certificate detection by Cert GCN and Text GCN. Figure 8: Accuracy, recall, and F1 values for Cert GCN and Text GCN malicious certificate classifiers. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **F1** \\ \hline Cert GCN & 97.41\% & 69.75\% & 81.29\% \\ Text GCN & 93.55\% & 53.70\% & 68.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of model results of malicious certificate detection by Cert GCN and Text GCN. Figure 9: Accuracy, recall, and F1 values for Cert GCN and Text GCN benign certificate classifiers. We found that Cert GCN does not perform better with more layers in classification compared with other neural networks.