Automated Static Analysis and Classification of Android Malware using Permission and API Calls Models

P. 243–252.

Skovoroda A., Gamaunov D.

In this paper we propose a heuristic approach to static analysis of Android applications based on matching suspicious applications with the predefined malware models. Static models are built from Android capabilities and Android Framework API call chains used by the application. All of the analysis steps and model construction are fully automated. Therefore, the method can be easily deployed as one of the automated checks provided by mobile application marketplaces or other interested organizations. Using the proposed method we analyzed the Drebin and ISCX malware collections in order to find possible relationships and dependencies between samples in collections, and a large fraction of Google Play apps collected between 2013 and 2016 representing benign data. Analysis results show that a combination of relatively simple static features represented by permissions and API call chains is enough to perform binary classification between malware and benign apps, and even find the corresponding malware family, with an appropriate false positive rate of about 3% (less than 1% in case of filtering adware). Malware collections exploration results show that Android malware rarely uses obfuscation or encryption techniques to make static analysis more difficult, which is quite the opposite of what we see in the case of the 'Wintel' endpoint platform family. We also provide the experiment-based comparison with the previously proposed state-of-the-art Android malware detection method adagio.

Keywords: information security, static analysis, analytical models, Android malware

In book

2017 15th Annual Conference on Privacy, Security and Trust (PST)

IEEE, 2017.