Towards Secure Mobile Learning. Visual Discovery of Malware Patterns in Android Apps

Due to the diffusion of mobile devices, more and more people access e-learning platforms from mobile phones. Students learn from digital books and have access to information anytime and anywhere. However, with billions of mobile users worldwide, as well as billions of under-protected Internet of Things (IoT) devices, the risk of being the target of malware, cybercrime and sophisticated attacks is high. This paper proposes and discusses a set of visualization techniques applied to a dataset generated by DREBIN, a malware detection tool that performs a static analysis on apps installed to Android devices. On the base of dataset, we applied text, tree and graph visualization techniques to identify malware patterns. The visual findings can help the cybersecurity analyst in detecting malicious app behavior.