# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 5 Experimental Setup and Results
### RGB Channel Contribution Analysis
This suggests that the high performance of our model is due to the RGB-specific feature extraction branch, which will be discussed below, rather than this specific component. The most striking finding is that the model performance drops from 98.45% to 3.88% in the test without the RGB-specific feature extraction branch. This dramatic drop proves that the RGB branch, which is the cornerstone of our proposed architecture, plays an indispensable role in the model to perform accurate classification, and the model's performance almost entirely depends on the features provided by this component. Figure 10 visually confirms the results of the ablation study. Without the RGB branch, the model fails to identify a meaningful focus area, whereas our full model focuses accurately on the object. This demonstrates that the RGB branch is critical not only for accuracy, but also for the model to make the right'reasons' decision. As shown in Figure 11, each square represents a filter trained to detect basic visual primitives. The filters demonstrate a sensitivity to color gradients (e.g., transitions from dark to red), specific color blobs (e.g., solid green or blue areas), and simple textures or edge-like patterns. This indicates that the RGB branch learns to decompose the image into fundamental, color-sensitive features from the very first layer, providing a meaningful basis for the subsequent, more complex feature extraction. To further investigate why the RGB-specific branch is so critical to the model's performance, we visualized the filters learned by its first convolutional layer, as shown in Figure 11. Unlike the complex, multi-channel filters learned by standard backbones, our RGB branch learns a set of fundamental, interpretable features. Many filters specialize in \begin{table}
\begin{tabular}{l l} \hline \hline
**Model Configuration** & **Accuracy (\%)** \\ \hline Full Model (Proposed) & 98.45 \\ \hline Ablated: RGB-Specific Branch Removed & 3.88 \\ \hline Ablated: Channel Attention Removed & 98.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Ablation study results. detecting simple color transitions and textures, acting as basic color-sensitive edge or blob detectors. This foundational feature extraction allows the model to build a robust hierarchical representation based on color information, a crucial element that, as our ablation study demonstrates, is indispensable for achieving high accuracy on this task. Analysis of Model Complexity Using the PQS-FP (Parameter Quantity Shifting-Fitting Performance) Framework  
To rigorously evaluate the relationship between our model's complexity and its performance, we conducted a systematic analysis using the Parameter Quantity Shifting-Fitting Performance (PQS-FP) coordinate system, as suggested by recent literature [54; 55]. This  
Figure 10: Gran-CAM comparative samples. framework provides theoretical insights into a model's efficiency and scalability by mapping its behavior (under-fitting, good-fitting, over-fitting) based on adjustments in its parameter quantity. We systematically varied the complexity of our model's classifier head, creating six distinct configurations with an increasing number of parameters, ranging from a simple single-layer classifier ([128]) to a complex three-layer classifier ([2048, 1024, 512]). Each configuration was trained under identical conditions, and we recorded its total parameter count (P), its best validation loss, and the corresponding training loss. The optimal parameter quantity (P_opt) was defined as the parameter count of the model that achieved the lowest validation loss. The quantitative results of our experiments are presented in Table 16, and these results are visualized on the PQS-FP coordinate system in Figure 12.