# Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification +
## 5 Discussion  
Unfortunately, due to hardware limitations, we were not able to experiment with the quantum models (i.e., Hybrid-QCNN and the QNN) using images with dimensions bigger than 25 \(\times\) 1. Anyway, when we trained the quantum and convolutional models by using the same image dimensions, we can note that the quantum models can obtain better performances. This result is highlighted in Figure 2, in particular by observing the results obtained from the EfficientNet(25 \(\times\) 3) and Hybrid-QCNN networks, with an accuracy, respectively, equal to 0.251 (for the EfficientNet model) and 0.905 (for the Hybrid-QCNN one). The Standard-CNN model, with images of a size of 25 \(\times\) 3, obtained an accuracy equal to 0.915, which is slightly higher if compared with the one obtained from the Hybrid-QCNN ones, but the Standard-CNN model was trained with 64 as the dimension of the batch, while the Hybrid-QCNN with a batch size equal to 32. For these reasons, we observed that quantum Machine Learning can be promising in the malware detection task. After the execution of each experiment, we decided to apply the Grad-CAM algorithm to the best convolutional and quantum models, i.e., the Standard-CNN and Hybrid-QCNN ones. Using this algorithm, it is simple to understand which areas of one image are most influential at the end of the discrimination. Below, we report some examples of a single image that included the PNG image of the file, the activation map, and the image generated by overlaying the initial PNG image with its activation map. The activation map consisted of three unique colors: yellow, green, and blue. The regions colored yellow symbolize the most interesting area, while green designates the sections in the center. Finally, to highlight a section of the image that is unrelated to the model, we used the blue shadow. In Figure 8, we can see the detection of a sample belonging to the Airpush family with a high precision of 100%. As is possible to see, the most significant area is at the top of the image. In fact, there, the pixels are overlayed with the yellow shadow, while other parts of the image do not appear to be very important to classify the malware. As is described in Section 4, the models can make mistakes in terms of identification. Figure 9 show the wrong classification of malware belonging to the Airpush family recognized as a member of the Downgin family with an accuracy slightly less than 100% (99.9%). Figure 8: Sample classified as belonging to the Airpush family (4a985c341 elee08f647395d00640c1f2) with 100%, where we can note that the yellow areas are on the upper side of the image, obtained with the model built using the Standard-CNN network. In Figure 10, we report an example of the classification of a Dowgin malicious sample with a precision value equal to 100%. In that picture, it is possible to understand why in eighteen cases of the images related to this family were classified as Airpush. In fact, as is possible to see in Figures 8 and 10, pixels at the top of the images are highlighted with the yellow shadow. Instead, in Figure 11, the models are classified as Trusted dangerous applications belonging to the Dowgin family. In conclusion, we can affirm that, as it did not achieve higher results, using the Hybrid-QCNN makes more mistakes during the classification than the Standard-CNN model. In addition, thanks to the usage of the Grad-CAM algorithm, even inexperienced people can understand where the model has confusion. Assuming that malware belonging to the same family shares the malicious payload (i.e., the dangerous action), the idea at the bottom of Grad-CAM is to highlight similar areas in the images. Differently, samples belonging to different families should exhibit different areas highlighted by Grad-CAM. The rationale is to find regions of the images obtained from malware symptomatic of certain malicious behaviors: in this way, the security analyst can focus his/her manual effort on a reduced section of the application under analysis.