Evaluation of Android Malware Detection Based on System Calls


Marko Dimjasevic, Simone Atzeni, Ivo Ugrina, Zvonimir Rakamaric. 2nd ACM International Workshop on Security and Privacy Analytics (IWSPA 2016), New Orleans, LA, USA.
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Abstract: With Android being the most widespread mobile platform, protecting it against malicious applications is essential. Android users typically install applications from large remote repositories, which provides ample opportunities for malicious newcomers. In this paper, we evaluate a few techniques for detecting malicious Android applications on a repository level. The techniques perform automatic classification based on tracking system calls while applications are executed in a sandbox environment. We implemented the techniques in the MALINE tool, and performed extensive empirical evaluation on a suite of around 12,000 applications. The evaluation
considers the size and type of inputs used in analyses. We show that simple and relatively small inputs result in an overall detection accuracy of 93% with a 5% benign application classification error, while results are improved to a 96% detection accuracy with up-sampling. Finally, we show that even simplistic feature choices are effective, suggesting that more heavyweight approaches should be thoroughly (re)evaluated.

Note: We made our full data set for this paper publicly available and you can download it from zenodo. An extend version of this paper is available as a technical report.


  author = {Marko Dimja\v{s}evi\'c and Simone Atzeni and Ivo Ugrina and Zvonimir Rakamari\'c},
  title = {Evaluation of {Android} Malware Detection Based on System Calls},
  booktitle = {Proceedings of the 2nd ACM International Workshop on Security and
    Privacy Analytics (IWSPA)},
  publisher = {ACM},
  year = {2016},
  pages = {1--8},