# CAA-RF: An Anomaly Detection Algorithm for Computing Power Blockchain Networks
## 4 Experimental Results and Analysis
### Performance Evaluation and Comparative Analysis  
Considering the computational cost of the model, we have evaluated our model from multiple perspectives, including CPU utilization, memory consumption, average training time per epoch, space complexity, time complexity, and FLOPS (Floating Point Operations Per Second). Overall, the CAA-RF model achieves a good balance between computational resource consumption and performance. Considering that some experimental models cannot be trained on GPUs, all models in this study are trained on CPUs to ensure fair and convenient comparison. We specifically compared the computational resource usage of different models. Our CAA-RF model demonstrates moderate CPU utilization, memory consumption, and FLOPS, with only the average training time per epoch being relatively high, mainly due to the higher model complexity. The specific results are shown in Table 3 below. In Figure 6, the eight bar charts represent anomaly detection on vehicular blockchain traffic data. The first is the VAE model, which has the worst performance among all models. Compared to other models, only two models outperform our proposed model, which combines random forest, and it also achieves a higher accuracy than using random forest alone. \begin{table}
\begin{tabular}{c c c c} \hline
**Data** & **0** & **1** & **2** \\ \hline Train & 11,334 & 4753 & 5365 \\ Test & 2810 & 1171 & 1382 \\ \hline \end{tabular}
\end{table} 
Table 2: Dataset statistics. In this experiment, we compared the F1 scores, training accuracy, and testing accuracy of various deep learning models, as shown in Figures 7-9. In Figures 7-9, subfigure (a) shows the changes in model performance with respect to training epochs, while subfigure (b) shows the changes in model performance over time. The differences between these plots depend on the training duration per epoch for each model, which helps demonstrate the models' operational efficiency and computational overhead. Within the 500-s time window, the number of epochs included for each model is as follows: VAE: 910; RNN: 981; autoencoder: 146; LSTM: 702; GANs: 98; DBN: 589; CAA: 25; and CAA-RF: 25. As shown in Figure 7b, the first data point for some models does not appear at seconds. This is because the model undergoes initialization and requires a relatively long training time during the first 50 s after training begins, so the first data point is generated several seconds later. As a result, the initial data point of the curve appears at the 50-s mark instead of at seconds. As shown in Figure 7, the F1 score versus training rounds and time (s) curve is illustrated. The horizontal axis represents the number of training iterations and training time (s), while the vertical axis indicates training accuracy. The results reveal that Generative Adversarial Networks (GANs), Long Short-Term Memory (LSTM) Networks, and CAARF consistently demonstrated excellent F1 scores throughout the training process, particularly after a certain number of iterations, where their advantages became more apparent. In contrast, models such as Variational Autoencoders (VAEs) and Recurrent Neural Networks (RNN) are also able to learn the model's performance.