## 4 Evaluation and Results
### Ablation Study  
To evaluate the contribution of each APK component in the proposed YoloMal-XAI framework, we conducted a comprehensive ablation study across both binary and multi-class classification tasks. Seven configurations were tested: DEX only, Manifest only, Resources only, DEX + Manifest, DEX + Resources, Manifest + Resources, and the full RGB combination incorporating all three. The RGB channel mappings for each configuration are detailed in Table 12, and example image representations are shown in Figure 8, highlighting structural differences across feature combinations. The results, presented in Table 13, reveal that the DEX component alone achieves remarkably high performance, with 98.64% accuracy in binary classification and 96.77% in multi-class classification. This confirms that the Dalvik bytecode remains the primary feature carrying discriminative information for malware detection, as it encodes the executable logic and API calls characteristic of malicious behaviours. In contrast, the Manifest.xml component shows moderate performance, achieving 96.29% binary accuracy and \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Task** & **Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Training Time (h)** \\ \hline \multirow{2}{*}{Binary} & YOLO11x & 99.87 & 99.92 & 99.92 & 99.92 & 6.42 \\  & YOLOv8x & 99.91 & 99.92 & 99.96 & 99.94 & 22.02 \\ \hline \multirow{2}{*}{Multi-Class} & YOLO11n & 99.56 & 99.56 & 99.56 & 99.56 & 1.07 \\  & YOLOv8n & 98.87 & 98.88 & 98.87 & 98.86 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance comparison between YOLO11 and YOLOv8 for binary and multi-class classification tasks. Figure 8: Visual comparison of image encodings based on different APK components: (left to right) DEX only, Manifest only, Resources only, and their pairwise and full combinations. Each RGB configuration reveals unique structural features used by the classifier. 91.64% multi-class accuracy, indicating that while permission and component declarations provide useful indicators of suspicious activities, they lack detailed behavioural semantics to support precise malware family classification. Resources.arsc alone yields 98.99% binary classification accuracy but only 91.83% in multi-class tasks, suggesting that static resources such as icons, layouts, and strings can reflect repackaging or branding modifications typical in malware repackaging attacks, yet remain insufficient for distinguishing between malware families. Combining DEX with either Manifest or Resources leads to significant performance improvements, demonstrating the complementary nature of semantic and structural cues encoded in these APK components. For instance, the combination of DEX and Resources achieves 99.33% binary and 98.57% multi-class accuracy, while DEX and Manifest achieve 99.08% and 98.07%, respectively. To visualize the training behavior across different feature combinations, Figure 9 presents the Top-1 accuracy curves for binary and multi-class classification. The curves confirm that configurations involving the DEX component converge faster and more stably, with the full RGB combination (DEX + Manifest + Resource) consistently achieving the best performance.