## 4 Evaluation and Results
### Results  
We assess YoloMal-XAI's performance across binary and multi-class classification tasks to evaluate its robustness and scalability using all YOLO11 variants. Table 9 presents a unified comparison of classification accuracy, precision, recall, F1-score, and training time for both settings. In the binary classification task, YOLO11x achieves the best performance overall (99.87% accuracy, 99.92% F1-score), benefiting from its increased capacity, albeit at a high computational cost (6.42 h). YOLO11s and YOLO11m yield identical F1-scores (99.83%), with YOLO11s requiring significantly less training time (1.17 h vs. 2.04 h), making it a more efficient mid-range option. YOLO11n demonstrates excellent performance given its compact architecture, achieving 99.68% accuracy in just 0.81 h--ideal for real-time or resource-constrained deployments. In the multi-class scenario, YOLO11n leads with the highest accuracy (99.56%) and F1-score (99.55%), outperforming deeper models despite its smaller size. Larger variants such as YOLO11x (98.90%) and YOLO11m (99.15%) show a slight drop in performance while incurring longer training times. YOLO11s and YOLO11 offer balanced trade-offs, with accuracy around 99.3-99.4% and moderate training duration. These results suggest that YOLO11n is better suited for fine-grained multi-class malware categorization, while YOLO11x excels in binary discrimination when computational cost is not a constraint. We analyze the training dynamics of YOLO11 variants of YOLO11x, which achieved the highest binary classification performance, is shown in Figure 3, highlighting both its accuracy and loss over epochs. In contrast, Figure 4 presents the training behavior of YOLO11n, which demonstrated superior generalization in the multi-class setting. To evaluate the classification performance in detail, we present the confusion matrices of the best-performing YOLO11 variants in both tasks. Figure 5 shows the binary confusion matrix for YOLO11x and the multi-class confusion matrix for YOLO11n. For binary classification, YOLO11x correctly identified 803 benign and 2365 malware samples, with only 2 false positives and 2 false negatives--highlighting a low false-positive rate, which  
Figure 4: Learning curves for YOLO11n on multi-class classification: (**a**) accuracy and (**b**) loss. Figure 3: Learning curves for YOLO11x on binary classification: (**a**) accuracy and (**b**) loss. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Binary Classification**} & \multicolumn{5}{c}{**Multi-Class Classification**} \\ \cline{2-11}
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Training** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Training** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **Time (h)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **Time (h)** \\ \hline YOLO11n & 99.68 & 99.87 & 99.70 & 99.79 & 0.81 & **99.56** & **99.51** & **99.58** & **99.55** & **1.07** \\ YOLO11s & 99.75 & 99.79 & 99.87 & 99.83 & 1.17 & 99.43 & 99.29 & 99.15 & 99.22 & 1.13 \\ YOLO11m & 99.75 & 99.79 & 99.87 & 99.83 & 2.04 & 99.15 & 99.17 & 98.92 & 99.04 & 2.06 \\ YOLO11n & 99.81 & 99.96 & 99.79 & 99.87 & 2.71 & 99.34 & 99.28 & 99.04 & 99.16 & 2.72 \\ YOLO11x & **99.87** & **99.92** & **99.92** & **99.92** & **6.42** & 98.90 & 98.82 & 98.34 & 98.57 & 6.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of YOLO11 variants on binary and multi-class malware classification. is crucial for security applications. In the multi-class case, YOLO11n achieved 3158 correct predictions across the five malware categories, with minor misclassifications such as 3 Riskware instances predicted as Banking and 2 SMS samples misclassified as Banking, indicating slightly overlapping feature representations in these classes. To further assess the discriminative capability of YoloMal-XAI, we analyze the Receiver Operating Characteristic (ROC) curves for both binary and multi-class classification tasks.