## 5 Experiment
### Experiment with the Interpretation Process
It has been shown that malicious applications belonging to the same malware family reveal similar patterns on the heatmap. In another study, [PERSON] et al. [8], an opcode sequence was extracted from the application and used as input. They calculated the local interpretable model-agnostic explanations (LIME) activation and created a line graph image of the activation. The image shows which opcode sequences are highlighted. The other study, XMal, proposed by [PERSON] et al. [9], used API calls and permissions as features. It obtained APIs and permissions from APKs through a preprocessing process. XMal did not use all APIs and permissions but selected 97 APIs and 61 permissions. Then, through a customized model consisting of a fully connected network and attention mechanism, malicious classification was performed and APIs and permissions that could explain malicious behavior were selected. XMal [9] used a metric similar to the F1-score (Equation (16)) to quantitatively measure how well a model describes the behavior of a malicious application while expressing the interpretation results in natural language. Therefore, we compared XMal and the method \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **Year** & **Model** & **Features** & **Explanation Method** & **Explanation Result** \\ \hline [PERSON] et al. [7] & 2021 & CNN & DEX grayscale Image & Grad-CAM & cumulative heatmap image \\ [PERSON] et al. [8] & 2021 & CNN & opcode sequence & LIME & Visualizing activations \\ [PERSON] et al. [9] & 2021 & XMal & API calls and Permissions & customised attention & natural language descriptions \\ Our approach & - & ViT & DEX RGB Image & attention mechanism & String, Class/Method Name \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Studies that provide the basis for malicious app detection or extract the functionality of malicious apps through the interpretation of malware detection models. Figure 5: Attention points changed to DEX offset values. (**a**) is a 2D image of the _dex_mm_list_ generated by Algorithm 2. (**b**) is a selected high attention region of the attention map. proposed in this study with a numerical value. The following Equations (21)-(23) are used to measure interpretability. \[precision_{description}=\frac{detect\_concepts}{detect\_concepts+surplus\_concept} \tag{21}\]  
\[recall_{description}=\frac{detect\_concepts}{total\_concepts} \tag{22}\]  
\[ir_{description}=\frac{2\times precision_{description}\times recall_{description}}{ precision_{description}+recall_{description}} \tag{23}\]  
where \(precision_{description}\) is the proportion of \(detect\_concepts\) among the concepts in the generated descriptions. \(recall_{description}\) is the proportion of \(detect\_concepts\) among the concepts in the ground truth. \(detect\_concepts\) is the number of \"concepts\" in the ground truth detected by model. \(surplus\_concept\) is the number of \"concepts\" in the generated descriptions that do not exist in the ground truth. \(total\_concepts\) is the total number of \"concepts\" in the ground truth. For a detailed explanation of Equation (23), refer to [PERSON] et al. [9]. Table 8 shows an example of ground truth. The ground truth for malicious samples, which consists of phrases describing the behavior of malicious applications, was generated by security experts. We used the ground truth provided by XMal in our study. The process of calculating \(ir_{description}\) is as follows. For apps identified as malicious by our model, we extracted the names of the classes and methods using the proposed method. API and permissions are the key features. The API calls in the source code of the extracted class, and the permissions required by the API are extracted. If the extracted class is defined in AndroidManifest.xml, the permissions corresponding to the described intent action are collected. The maximum number of extracted key features is six. The extracted key features are changed to semantics using the rules provided by XMal.