On the Effectiveness of System API-Related Information for Android Ransomware Detection

Ransomware constitutes a significant threat to the Android operating system. It can either lock or encrypt the target devices, and victims are forced to pay ransoms to restore their data. Hence, the prompt detection of such attacks has a priority in comparison to other malicious threats. Previous works on Android malware detection mainly focused on Machine Learning-oriented approaches that were tailored to identifying malware families, without a clear focus on ransomware. More specifically, such approaches resorted to complex information types such as permissions, user-implemented API calls, and native calls. However, this led to significant drawbacks concerning complexity, resilience against obfuscation, and explainability. In this paper, we propose a different, static approach to accurately detect Android ransomware, which is independent of user-defined information and that leverages the fact that ransomware attacks heavily resort to System API to perform their actions. More specifically, by only using System API-based information, it is possible to detect ransomware accurately and to distinguish it from generic malware and goodware. To this end, we proposed and tested three different ways of employing System API by using packages, classes, and methods, and we compared their performances to other, more complex state-of-the-art approaches. The attained results showed that systems based on System API could detect ransomware and generic malware with very good accuracy, comparable to systems that employed more complex information. Moreover, the proposed systems could accurately detect novel samples in the wild and showed resilience against static obfuscation attempts. Finally, to guarantee early on-device detection, we developed and released on the Android platform a complete ransomware and malware detector that employed one of the methodologies proposed in this paper.