# Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification +
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Image Size** & **Batch** & **Epochs** & **Learning Rate** &
\begin{tabular}{c} **Training Time** \\ **(HH:MM:SS)** \\ \end{tabular} \\ \hline AlexNet & \(110\times 1\) & 32 & 50 & 0.001 & 0:47:14 \\ \hline Standard-CNN & \(110\times 1\) & 32 & 50 & 0.001 & 1:11:54 \\ \hline Standard-CNN & \(25\times 3\) & 64 & 20 & 0.001 & 0:01:34 \\ \hline VGG16 & \(110\times 3\) & 32 & 25 & 0.001 & 2:56:45 \\ \hline MobileNet & \(110\times 3\) & 64 & 25 & 0.001 & 0:22:45 \\ \hline VGG19 & \(110\times 3\) & 64 & 25 & 0.001 & 3:35:56 \\ \hline EfficientNet & \(110\times 3\) & 64 & 25 & 0.001 & 0:36:25 \\ \hline EfficientNet & \(25\times 3\) & 64 & 20 & 0.001 & 0:07:26 \\ \hline Hybrid-CNN & \(25\times 1\) & 32 & 20 & 0.010 & 1 day, 5:39:14 \\ \hline QNN & \(4\times 1\) & 64 & 10 & 0.010 & 0:10:34 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The hyperparameters of the models employed in the experimental analysis to compare convolutional and quantum deep learning networks for Android malware detection. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Loss** & **Accuracy** & **Precision** & **Recall** & **F-Measure** & **AUC** \\ \hline AlexNet & 0.435 & 0.912 & 0.920 & 0.908 & 0.914 & 0.983 \\ \hline Standard-CNN (\(110\times 3\)) & 0.205 & 0.970 & 0.972 & 0.970 & 0.971 & 0.993 \\ \hline Standard-CNN (\(25\times 3\)) & 0.260 & 0.915 & 0.927 & 0.910 & 0.919 & 0.993 \\ \hline VGG16 & 0.182 & 0.952 & 0.959 & 0.949 & 0.954 & 0.993 \\ \hline MobileNet & 0.124 & 0.966 & 0.969 & 0.963 & 0.966 & 0.996 \\ \hline VGG19 & 0.187 & 0.951 & 0.953 & 0.947 & 0.950 & 0.994 \\ \hline EfficientNet (\(110\times 3\)) & 23.738 & 0.065 & 0.065 & 0.065 & 0.065 & 0.455 \\ \hline EfficientNet (\(25\times 3\)) & 2.343 & 0.251 & 0.135 & 0.022 & 0.038 & 0.535 \\ \hline Hybrid-QCNN & 1.045 & 0.905 & 0.905 & 0.903 & 0.904 & 0.962 \\ \hline QNN & 1.584 & 0.413 & 0.623 & 0.103 & 0.176 & 0.762 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results obtained with all the models involved in the evaluation by exploiting the test dataset. In Table 2, we report the best results obtained from several experiments. From it, we can see that the best model is the Standard-CNN, with a precision equal to 0.972 and a recall value of 0.970. In addition, also good performances were obtained using the MobileNet, VGG16, and VGG19 models. The Hybrid-QCNN model exhibited a precision equal to 0.905 and a recall of 0.903, while the fully quantum model, labeled as QNN, obtained lower results, such as a precision value of 0.623 and a recall value of 0.103. Due to the limited computing resources, to perform a better comparison between the fully convolutional and hybrid models, we also executed experiments using Standard-CNN and EfficientNet with a size of 25 \(\times\) 25 \(\times\) 1. From Table 2, we can see, albeit by a little, that the Standard-CNN model performed better than the Hybrid model. We can also say that the results obtained from the Hybrid models were far superior than the EfficientNet results. In addition, it is worth noting also the interesting results obtained after the usage of the AlexNet model. Although it usually does not give back a high score for the metrics, in this case, we achieved a precision value equal to 0.920 and a recall value equal to 0.983. We, respectively, report in Figures 6 and 7 the confusion matrix of the Standard-CNN and Hybrid-QCNN models. The images help to perform a more accurate direct comparison of the best deep learning model (in this work) and the quantum model. In Figure 6, we report the confusion matrix obtained from the _Standard-CNN_ model. In that, we can see that the Deep Learning model produced a small number of misclassifications. The higher number of items classified incorrectly was for the Airpush family, with 11 samples classified as Trusted.