On the effectiveness of system API-related information for Android ransomware detection

By Michele Scalas, Davide Maiorca, Francesco Mercaldo, Corrado Aaron Visaggio, Fabio Martinelli, Giorgio Giacinto. 2018

84 citations recorded by the source corpus.

Abstract 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. To overcome these issues, in this paper, we propose and discuss learning-based detection strategies that rely on System API information. These techniques leverage the fact that ransomware attacks heavily resort to System API to perform their actions, and allow distinguishing between generic malware, ransomware and goodware. We tested three different ways of employing System API information, i.e., through 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 (R-PackDroid) that employed one of the methodologies proposed in this paper.

Subfields: Computer Networks and Communications, Software, Signal Processing

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In Computer Networks and Communications

In Software

Giorgio Giacinto

In Signal Processing

Citations in the corpus

Adversarial Detection of Flash Malware: Limitations and Open Issues (2017, methodology): “In particular, some works employed code obfuscation through off-the-shelf tools or custom techniques [14, 15, 16, 17, 18] to perform evasion.”