Android Malware Detection Based on System Calls


Marko Dimjasevic, Simone Atzeni, Ivo Ugrina, Zvonimir Rakamaric. School of Computing, University of Utah, Tech Report UUCS-15-003, May, 2015.
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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 propose a simple, and yet highly effective technique for detecting malicious Android applications on a repository level. Our technique performs automatic classification based on tracking system calls while applications are executed in a sandbox environment. We implemented the technique in a tool called MALINE, and performed extensive empirical evaluation on a suite of around 12,000 applications. The evaluation yields 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. This indicates that our technique is viable to be used in practice. Finally, we show that even simplistic feature choices are highly effective, suggesting that more heavyweight approaches should be thoroughly (re)evaluated.


  author = {Marko Dimja\v{s}evi\'c and Simone Atzeni and Ivo Ugrina and Zvonimir Rakamari\'c},
  title = {Android Malware Detection Based on System Calls},
  institution = {School of Computing, University of Utah},
  year = {2015},
  month = {May},
  number = {UUCS-15-003},