Explanation-Driven Characterization of Android Ransomware

Machine learning is currently successfully used for addressing several cybersecurity detection and classification tasks. Typically, such detectors are modeled through complex learning algorithms employing a wide variety of features. Although these settings allow achieving considerable performances, gaining insights on the learned knowledge turns out to be a hard task. To address this issue, research efforts on the interpretability of machine learning approaches to cybersecurity tasks is currently rising. In particular, relying on explanations could improve prevention and detection capabilities since they could help human experts to find out the distinctive features that truly characterize malware attacks. In this perspective, Android ransomware represents a serious threat. Leveraging state-of-the-art explanation techniques, we present a first approach that enables the identification of the most influential discriminative features for ransomware characterization. We propose strategies to adopt explanation techniques appropriately and describe ransomware families and their evolution over time. Reported results suggest that our proposal can help cyber threat intelligence teams in the early detection of new ransomware families, and could be applicable to other malware detection systems through the identification of their distinctive features.