Download malware? no, thanks
Francesco Mercaldo, Vittoria Nardone, Antonella Santone, Corrado Aaron Visaggio · 2016
In mobile malware landscape there are many techniques to inject malicious payload in a trusted application: one of the most common is represented by the so-called update attack. After an apparently innocuous application is installed on the victim's device, the user is asked to update the application, and a malicious behavior is added to the application. In this paper we propose a static method based on model checking able to identify this kind of attack. In addiction, our method is able to localize the malicious payload at method-level. We obtain an accuracy very close to 1 in identifying families implementing update attack using a real Android dataset composed by 2,581 samples.