# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 5 Experimental Setup and Results
### RGB Channel Contribution Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Config** & \begin{tabular}{c} **P (Count of** \\ **Parameter**) \\ \end{tabular} & **PQS** & \
\begin{tabular}{c} **Best Val** \\ **Loss** \\ \end{tabular} &
\begin{tabular}{c} **Train Loss at** \\ **Best Val** \\ \end{tabular} & **FP (Val-Train)** & **Quadrant (Predict)** \\ \hline (128) & 11,264,406 & 0.00 & 0.0014 & 0.0002 & +0.0012 & Q2 (Optimal) \\ \hline (256, 128) & 11,592,854 & +0.029 & 0.0020 & 0.0001 & +0.0019 & Q2 (Over-fitting \& Efficient) \\ \hline (512, 256) & 12,285,206 & +0.091 & 0.0016 & 0.0002 & +0.0014 & Q2 (Over-fitting \& Efficient) \\ \hline (1024, 512) & 13,866,518 & +0.231 & 0.0023 & 0.0024 & \(-0.0001\) & Q3 (Good-fitting) \\ \hline (1024, 512, 256) & 13,992,214 & +0.242 & 0.0024 & 0.0002 & +0.0022 & Q1 (Over-fitting \& Redundant) \\ \hline (2048, 1024, 512) & 18,329,110 & +0.627 & 0.0023 & 0.0004 & +0.0019 & Q1 (Over-fitting \& Redundant) \\ \hline \hline \end{tabular}
\end{table} 
Table 16: The PQS-FP analysis. Figure 11: Visualization of Learned Filters in the First Convolutional Layer of the RGB-Specific Branch. Together, the table and figure provide a comprehensive view of the model's behavior, leading to several key insights:  
1. Optimal Model Efficiency: As detailed in Table 15, the model with the simplest classifier ((128)) achieved the lowest validation loss (0.0014). This configuration, with 11.26 million parameters, is therefore identified as our optimal model (P_opt). This is a significant finding, highlighted by its position as the origin point (PQS = 0) in Figure 12, demonstrating that our proposed architecture achieves its highest generalization performance in its most parameter-efficient state. 2. Dominance of Over-fitting: The \"FP\" column in Table 16, calculated as (Validation Loss-Training Loss), shows positive values for almost all configurations. This quantitatively confirms a tendency towards over-fitting. This is visually represented in Figure 12, where all data points are located in the upper half of the PQS-FP plane. The models achieve near-zero training loss, signifying an excellent fit to the training data, but this performance does not fully translate to the unseen validation data. 3. The Impact of Redundant Parameters: Analyzing the data in Table 16 and their positions in Figure 12 reveals that increasing model complexity (i.e., adding more parameters) does not guarantee better performance. As the Parameter Quantity Shifting (PQS) value increases, moving points into the Q1 (Over-fitting and Redundant) quadrant, the validation loss does not consistently decrease. For instance, the model with 18.3 million parameters ([2048, 1024, 512]) has a higher validation loss than the optimal model, confirming that the additional ~7 million parameters are redundant and do not contribute to better generalization. 4. Scalability and Performance Trade-off: The PQS-FP analysis provides a clear view of our model's scalability. The combined evidence from Table 16 and Figure 12 suggests that while the architecture is robust, adding complexity to the classifier does not scale  
Figure 12: PQS-FP coordinate system analysis of model complexity. performance linearly. The optimal point is located at the lower end of the complexity spectrum we tested. This implies that the feature extraction power of the EfficientNet-B0 and DenseNet-121 backbones is highly effective, and a complex classifier is not only unnecessary but can also be detrimental to generalization. In conclusion, the PQS-FP framework, supported by the detailed results in Table 16 and the visualization in Figure 12, has allowed us to move beyond a single performance metric. We have deeply understood the efficiency, scalability, and fitting behavior of our proposed method. The results strongly support that our model is most effective in a lean, parameter-efficient configuration, which is a desirable characteristic for practical deployment.