## 3 Proposed Approach
### Experimentation  
In the experimentation stage, we implement three models: Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Graph Attention Networks (GATs), which are recent advances in deep learning technologies, most useful in malware detection and addressing complex problems. Each of these models was selected based on its suitability for learning different characteristics of our dataset, which consists of 40,000 labeled samples (malware and benign). The three models each have different strengths:  
* The ANN model provides a free architecture to learn non-linear patterns, and ANNs are built with fully connected layers wherein every neuron connects to each neuron in nearby layers with optimal weights by applying activation functions. They are chosen for their ability to learn non-linear relationships in structured tabular data, which is essential for capturing the diverse patterns exhibited by different types of malware in our dataset. This flexibility allows ANNs to effectively distinguish between benign and malicious behaviors, even when malware variants present overlapping characteristics.
* The CNN model performs better in self-extracting features from spatial or structured data, and CNNs use convolutional filters to extract local features, automatically from structured data, such as images, progressively reducing the dimensionality of the data. They are employed for their capability to capture local spatial dependencies. CNNs are capable of automatically extracting local patterns from structured data, making them effective in detecting specific malware signatures. Their ability to reduce dimension while retaining essential information improves the detection of malicious behavior.
* The GAT model exploits graph-represented data connectivity using mechanisms to emphasize significant relations with attention processes to dynamically allocate weights to the importance of neighbors in information transfer throughout the graph.  
Figure 4: Discriminator loss graph.  
Figure 5: Generator loss graph.  
GATs are suitable for malware detection because they exploit the graph structure of malware, modeling the relationships between different signatures. Using the attention mechanism, they weigh the importance of connections to identify malicious behavior in complex structures.  
This experiment aims to compare these three models in malware detection, a field where data complexity and diversity demand robust and innovative solutions. Such a comparison will evaluate their performance on accuracy, strength, and ability to handle heterogeneous data, ultimately deciding the best approach for modern-day cybersecurity environments. Thus, we used four evaluation metrics to verify the performance of the models, which include accuracy, precision, recall, and the F1-score. All these are calculated by using the following equations:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[\text{Precision}=\frac{TP}{FP+TP} \tag{2}\]  
\[\text{Recall}=\frac{TP}{FN+TP} \tag{3}\]  
\[\text{F1-Score}=\frac{2\cdot\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{4}\]  
The main technologies and tools used to process the implementation and experimentation phases are presented and explained in Table 2.