## 4 Simulation and Results Discussions
### Analysis and Discussion  
The ANN model showed constant growth during the training period. Loss function reduction and the constant rise in slope regarding accuracy confirm that adequate learning has taken place on the model without a significant showing of over-fitting. Precision,  
Figure 14: GAT performance scores.  
Figure 12: GAT accuracy.  
Figure 13: GAT loss.  
recall, and F1-score plots corroborate this evidence through adequate convergence of marking impostors as positives and actual negatives. At a 97.60% accuracy, this excellent performance suggests that the ANN is particularly well adapted to classification problems, like malware detection.  
The CNN model also indicated improvement over training, if not as robustly for stability of performance across recall and F1-score as with the ANN. Although decreasing in the loss curve, the best accuracy was merely 80%. This suggests that although the model is good at classifying malware, it could benefit from optimization for hyperparameter tweaking or network structure in order to achieve a better stable balance between recall and precision and hence output a more consistent general performance.  
The GAT model was impressive with precision rates of 96.84% and an F1-score of 97.78%. Not only do these show strong generalization but also high robustness in precision and recall balance. The decline in loss and the consistent increase in performance indicators show the efficiency of the GAT in malware classification to deliver immense accuracy and reactivity. However, with such a superior performance, the application of GAT would be more complex with the need to process graph-structured information, which could go against its adoption over simpler models like ANNs.  
The comparison analysis of the performance of all three models, as explained in Table 3, confirms that the ANN model stands out as the most effective for malware detection, particularly due to its ability to maintain a good balance between precision and recall. To explain this performance outcome, we note that our dataset is structured and composed of various numerical and categorical features-derived malware. ANN models are particularly well suited for capturing complex and non-linear patterns, which aligns well with the nature of our feature-engineered malware dataset. This allowed the ANN to maintain a stable balance between precision and recall and achieve the highest generalization among the tested models. In contrast, the CNN and GAT models, while effective in other contexts, were less suited to the specific structure and dimensionality of our dataset. Hence, this stability renders it an ideal solution for mission-critical use cases such as protecting blockchain systems and the smart contract development environment. Blockchain transaction security relies on the continuous and stable monitoring of malware that may disrupt smart contracts. Because of its ability to generalize, the ANN is an effective solution for accurately identifying malicious behavior in such systems, thus offering an effective means of protecting the integrity of data and processes in the blockchain environment.