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Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

M. Spreitzenbarth, T. Schreck, F. Echtler, Daniel Arp, R.T. Hoffmann
Mobile-Sandbox: combining static and dynamic analysis with machine-learning techniques
International Journal of Information Security, Springer, April 2015 (bib)

Smartphones in general and Android in particular are increasingly shifting into the focus of cyber criminals. For understanding the threat to security and privacy, it is important for security researchers to analyze malicious software written for these systems. The exploding number of Android malware calls for automation in the analysis. In this paper, we present Mobile-Sandbox, a system designed to automatically analyze Android applications in novel ways: First, it combines static and dynamic analysis, i.e., results of static analysis are used to guide dynamic analysis and extend coverage of executed code. Additionally, it uses specific techniques to log calls to native (i.e., non-Java) APIs, and last but not least it combines these results with machine-learning techniques to cluster the analyzed samples into benign and malicious ones. We evaluated the system on more than 69,000 applications from Asian third-party mobile markets and found that about 21 % of them actually use native calls in their code.
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