Visual Discovery of Malware Patterns in Android Apps

The diffusion of smartphones and the availability of powerful and cheap connections allow people to access heterogeneous information and data anytime and anywhere. In this scenario billions of mobile users as well as billions of under-protected IoT devices have high risk of being the target of malware, cybercrime and attacks. This work introduces visualization techniques applied to software apps installed on Android devices using features generated by mobile security detection tools through static security analysis. The aim of this work is to help common people and skilled analysts to quickly identify anomalous and malicious software on mobile devices. The visual findings are reached through text, tree and other techniques. An app inspection tool is also provided and its usability has been evaluated with an experimental study with ten participants.