## 4 Results and Analysis
### Ablation Study  
The impact of each novel block has been meticulously examined through sophisticated experiments in this study. Initially, we stripped our proposed learning strategies, including FSAM, auxiliary branch, MHA, etc., from the proposed network architecture. Subsequently, we systematically reintroduced each proposed module to assess its impact on the final output. The base model excludes all novel components, including FSAM, auxiliary branch, and MHA. The FSAM variant includes an FSAM block with a base backbone. The SAMAUX variant adds an auxiliary branch over the FSAM variant, and DAMN comprises all novel components. Table 4 provides a comprehensive overview of the ablation study results, clearly demonstrating the meaningful impact of these modules on the reported final results.  
Moreover, the ablation results validate the feasibility and efficacy of these proposed modules in malware classification. By systematically analyzing the network's performance with and without each module, we gain valuable insights into the contributions of individual components to enhancing the overall classification accuracy. These findings underscore  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline VGG-16 [18] & 97.86 & 0.9641 & 0.9614 & 0.9778 \\ VGG-19 [18] & 98.29 & 0.9713 & 0.9703 & 0.9833 \\ AlexNet [51] & 95.08 & 0.8943 & 0.9116 & 0.9501 \\ Densenet121 [20] & 98.72 & 0.9806 & 0.9838 & 0.9875 \\ Efficientnet [76] & 97.75 & 0.9528 & 0.9537 & 0.9775 \\ GoogLenet [50] & 97.97 & 0.9629 & 0.9666 & 0.9802 \\ Mobilenet-v2 [19] & 98.40 & 0.9673 & 0.9607 & 0.9837 \\ Mobilenet-v3 [21] & 99.03 & 0.9773 & 0.9790 & 0.9906 \\ Resnet18 [22] & 99.03 & 0.9835 & 0.9819 & 0.9906 \\ Shufflenet-v2 [77] & 98.08 & 0.9637 & 0.9710 & 0.9806 \\ Squeezenet [78] & 98.29 & 0.9721 & 0.9712 & 0.9820 \\ Swin Transformer [79] & 97.86 & 0.9594 & 0.9609 & 0.9778 \\ VIT-B-16 [80] & 96.26 & 0.9273 & 0.9322 & 0.9622 \\ Wide ResNet [81] & 95.94 & 0.9350 & 0.9259 & 0.9598 \\ \hline
**DAMN (Proposed)** & **99.36** & **0.9892** & **0.9879** & **0.9924** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with state-of-the-art image classification models. The proposed DAMN can outperform the SOTA image classification methods in malware image classification.  
the importance of each novel block and provide helpful guidance for future research and development efforts in malware classification.  
Our proposed components offer enhanced accuracy and lightweight design. Despite significantly enhancing classification accuracy, these components impose minimal computational overhead. In summary, our innovative additions improve classification accuracy by 1.29% over the base variant, requiring only an additional 0.2 million trainable parameters.  
Apart from the objective scores, we visualize the training phase's validation accuracy and loss. As Figure 7 shows, our proposed DAMN is more stable than its other variants. Also, the dropout and our proposed auxiliary branch noticeably help our method to reduce overfitting and learn more valuable features among the experimented variants.