## 5 Experiment and Results Analysis
### Analysis of the Experimental Results  
Due to differences in the experimental environment, configuration, and dataset, it cannot be effectively compared with other models. In this paper, ablation experiments are conducted on the proposed model to demonstrate its effectiveness. Figure 5 visually presents the performance of the proposed method in this study compared to models such as the GRU and GRU-Attention. The experiment selected 17 weeks of network-security posture data and used four models to compare them with the real network-security posture values, respectively. As shown, we added the following analysis to Figure 5, and all of these prediction models have some predictive capability. However, the method proposed in this study achieves almost perfect correspondence in fitting between the predicted values and the real values. The prediction results on multiple data points all demonstrate better prediction performance of the GRU compared to the GRU model alone and the CNN model alone, highlighting the superiority of GRU in predicting time series. The prediction of the GRU-Attention model, after the addition of the CNN, shows a significant improvement, indicating that the introduction of a CNN can significantly enhance the model's predictive ability. The experimental results further confirm that the use of a model fusion strategy to integrate the advantages of different models can significantly improve the accuracy of predictions compared to using the CNN or GRU alone. This fusion method not only optimizes the prediction performance but also enhances the model's ability to capture data features, resulting in more accurate experimental results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Set up** \\ \hline Optimizer & Adam \\ Learning rate & 0.001 \\ Batch size & 1 \\ Epoch & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Setting of the training parameters.  
Figure 6 displays a trend plot of changes in model training error as the number of iterations increases. It can be observed that in the model with the attention mechanism, the convergence rate is significantly faster. This demonstrates that the inclusion of the attention mechanism can substantially enhance the convergence rate of the model. The prediction model proposed in this paper performs well in terms of convergence speed, confirming the effectiveness and superiority of the model in capturing temporal data features. Additionally, its prediction accuracy is excellent. In this paper, the model can effectively learn the key information from temporal data, demonstrating a faster convergence rate and higher accuracy during model training. This makes it an effective situation prediction model.  
Figure 5: Comparison of different models.  
Figure 6: Number of iterations of the different models.  
Table 4 lists the specific comparison results between the method proposed in this paper and several other methods in terms of mean-square error (MSE) and goodness of fit. Through the comparative analysis, we can clearly see that the model proposed in this study shows excellent performance in both key metrics of prediction error and goodness of fit, surpassing the other compared models, validating its application value in the domain of network-security situation prediction.  
Table 5 displays the absolute error of various prediction models at each time point. The data results further prove that the prediction method proposed in this research can achieve accurate prediction results in most cases. It can be observed from the data that when using the method proposed in this study, all the absolute errors are successfully controlled to an order of magnitude lower than 0.004. This performance showed higher prediction accuracy compared to the other models. Therefore, the prediction method of this study is capable of providing highly precise prediction results in most cases, with the error range consistently kept at a low level, demonstrating its advantage in prediction accuracy.