Exploring Quantum Machine Learning for Explainable Malware Detection

In recent times is increasing the number of cyberattacks. Daily, we employ computers and mobile devices to perform different tasks, and malicious users are always ready to perpetrate malicious actions. Usually, they use malicious applications able to gain personal information. To avoid these troubles researchers are always focused on the design of new ways to detect malware, typically exploiting machine learning. The progress and recent increasing adoption of quantum computing allowed the inclusion of quantum algorithms into machine learning methods. In this paper, we present a malware detection method based on a quantum machine learning model. In addition, to understand the explainability we adopted two different Class Activation Mapping algorithms to highlight the areas of the images under study that are responsible for a specific categorization of the malware's families. Moreover, we also compare the best results obtained using the quantum model we propose with one of the first convolutional neural network models, i.e., the Le-Net one, to understand whether quantum models can be promising in the malware detection task.