Towards Quantum Machine Learning in ransomware detection

Ransomware represent one of the most aggressive malware, due to their capability to prevent access to data and, as a consequence, totally paralyze the activity of any organization, such as companies, but also hospitals or banks. Considering the inadequacy of the signature-based approach, mainly exploited by free and commercial current antimalware, researchers are proposing new ransomware detection techniques based on deep learning. Recently, with the introduction of quantum computing, there is the possibility to introduce quantum principles into machine learning. In this paper, we propose an approach for ransomware detection through a quantum machine learning model aimed to analyse images obtained from the application opcodes. In particular, a hybrid model is proposed, composed of quantum and convolutional layers to discern between ransomware, generic malware, and trusted applications. To demonstrate that quantum machine learning is promising in ransomware detection, a real-world dataset composed by 15,000 applications is evaluated, by showing that the proposed hybrid quantum model obtains promising performances if compared to (fully) convolutional models (i.e., Alex Net, MobileNet, and a convolutional model developed by authors).