## 4 Experimental Results
### Discussion
 \\ \cline{5-5}  & & & & & 93.24\% \\ \hline  & & & & & & \\ \cline{5-5}  & & & & & & \\ \cline{5-5}  & & & & & & \\ \cline{5-5}  & & & & & & \\ \hline  & & & & & & \\ \cline{5-5}  & & & & & & \\ \cline{5-5}  & & & & & & \\ \cline{5-5}  & & & & & & 93.6\% \\ \hlineThe comparison of the studies in the literature on classification by generating images from Android features and the developed system information are shown in Table 9. In the studies carried out, it was seen that the analyses were carried out on ready-made datasets. In the studies examined, the Drebin [58] and AMD [59] datasets were widely used. In our studies, the CICMalDroid 2020 [63; 64] dataset was used because it has more up-to-date Android applications. It was observed that static features were generally used in the studies conducted by generating images and the CNN method was used for classification. Furthermore, in the examined studies, machine learning algorithms and the Vision Transformer (ViT) algorithm were used for classification. In the study by [PERSON] et al. [37], three convolutional layers were followed by a max-pooling layer after each convolutional layer, and a total parameter count of 37,975,105 was achieved. [PERSON] et al. [40] utilised three convolutional layers, each followed by a max-pooling layer. [PERSON] et al. [44] employed two convolutional layers, each followed by a max-pooling layer. In the study by [PERSON] et al. [50], three convolutional layers followed by max-pooling layers were used. [PERSON] et al. [51] employed four convolutional layers followed by one max-pooling layer. [PERSON] et al. [56] utilised three convolutional layers and two pooling layers. In our study, we employed two convolutional layers followed by a max-pooling layer after each convolutional layer, resulting in a total parameter count of 3,258,799. It is observed that the developed model has low complexity. Unlike the CNN architecture used in other studies, our model is developed \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**References** & **Datasets** & **Analysis Method** & **Features** & **Creating Image** & **Algorithms** & **Classification** \\  & & & & & **Method** & **Performance** \\ \hline
[51] & AMD & static & \
\begin{tabular}{c} small file in API \\ sequence with \\ protection levels \\ \end{tabular} & API code to image & CNN &
\begin{tabular}{c} Accuracy \\ 92.84\% \\ \end{tabular} \\ \hline
[56] & - & static & API calls & \
\begin{tabular}{c} System call \\ sequence into an RGB image \\ (colour values \\ matching from the \\ constructed feature \\ database) \\ \end{tabular} & CNN &
\begin{tabular}{c} Accuracy \\ 92.9\% \\ \end{tabular} \\ \hline
[57] & Drebin & static & \
\begin{tabular}{c} Opcodes and API \\ calls \\ \end{tabular} & ASCII code and dictionary & CNN &
\begin{tabular}{c} Precision \\ 93.36\% \\ \end{tabular} \\ \hline
\begin{tabular}{c} Accuracy \\ 91\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._with fewer convolutional layers and is designed to be simpler and more understandable. This aims to run the model with less GPU and CPU usage. The studies showed that the images used varied in size. The image dimensions for each study are presented in Table 10.  
The features were used for the classification process, and the classification success varies between 90% and 99% according to the Accuracy value, the classification success varies between 87.01% and 97.9% according to the F1-score value, the classification success varies between 85.9% and 97.8% according to the Precision value, and the classification success varies between 86.81% and 98.1% according to the Recall value. It was seen that the success of the study was close to or higher than the success of other studies. In the studies given in Table 9, it is generally seen that the file itself is produced as a binary reading.