# CAA-RF: An Anomaly Detection Algorithm for Computing Power Blockchain Networks
## 4 Experimental Results and Analysis
### Performance Evaluation and Comparative Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **CPU** & \multirow{2}{*}{**Memory (MB)**} & \multicolumn{2}{c}{**Average**} & \multirow{2}{*}{**Space**} & \multirow{2}{*}{**Time**} & \multirow{2}{*}{**FLOPS**} \\  & **Utilization (\%)** & & & & & \\ \hline CAA & 14.2 & 3271.8 & **20.16657** & 636187 & 2560000000 & 1269427394 \\ CAA-RF & 15.4 & 3498.3 & **20.40496** & 674587 & 27358208000 & 1340762638 \\ Auto & 11.4 & 3414.7 & 0.408983 & 4117 & 246,180,000 & 601,931,492 \\ GANs & 9.7 & 3428.5 & 4.951626 & **246,186** & 352,480,000 & 71,184,693 \\ LSTM & **19.2** & **5546.3** & 0.810934 & **206,211** & **8,207,360,000** & **10,120,866,978** \\ RNN & **36.4** & 3532.9 & 0.050418 & 32,103 & **1,276,000,000** & **25,308,412,560** \\ VAE & 10.4 & 3461.7 & 0.615854 & 2322 & 87,040,000 & 141,332,137 \\ DBN & 11.7 & **5154.2** & 0.764012 & 12,355 & 485,120,000 & 634,963,849 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model computational cost. Figure 6: Comparison of the accuracy of multiple neural networks. Networks (RNNs) performed less effectively in the early stages but showed significant improvement as training progressed. Figure 8 shows the curve of training accuracy versus training rounds and time. The left graph shows that the CAARF model performs excellently in both training rounds and training time. Initially, the training accuracy reached 100%. In the early stages of training, due to the limited number of epochs, the CAA-RF model is unable to effectively extract complex features, and currently, the testing accuracy remains low. However, the secondary training by the random forest temporarily raises the training accuracy on the training set to 1.0. As training progresses, the CAA model gradually adjusts its parameters to more accurately extract multiple complex features from the data. The fitting ability of the random forest for these complex features diminishes, resulting in a decre  
Figure 8: (**a**) Training accuracy curves with round and (**b**) Training accuracy curves with round time. Figure 7: (**a**) F1 score curve with round and (**b**) F1 score curve with time. Figure 9: (**a**) Test accuracy curves with round and (**b**) Test accuracy curves with time. However, this helps improve the model's generalization ability and, thus, increases the testing accuracy. This phenomenon is precisely the result of the dual-structure design of the CAA-RF model. In contrast, the training accuracy curves of other single-layer models are more stable and conventional. Figure 8 shows the curve of testing accuracy versus training rounds and time. The left graph shows that at around 20 training rounds, the CAARF model achieved its highest testing accuracy, slightly lower than LSTM, but still significantly better than other models. **In addition, the right graph shows poorer results, with the \(x\)-axis representing time in seconds. ** In the curve of testing accuracy versus time, as shown in Figure 9, it can be observed that the CAARF model's testing accuracy is relatively lower in the early stages compared to other models. This phenomenon is mainly due to the longer training time required for each cycle of the CAARF model. The structure of the CAA-RF model is relatively complex. Its design combines convolutional neural networks, adaptive mechanisms, self-attention mechanisms, and random forests to perform multi-dimensional detection of anomalous behaviors in blockchain networks. The synergy of these modules enhances the model's detection performance but inevitably increases computational complexity, resulting in longer training times. Within the same observation period, the model has not yet completed sufficient training. Therefore, although the CAARF model theoretically has higher potential, its testing performance did not match that of other models in the short term due to the limitation in training efficiency.