Abstract
Dynamic analyzing techniques play an important and unique role in detecting Android malware and vulnerabilities, as they can provide higher precision than static methods. However, they are inherently incomplete and inefficiency. We attack this problem by proposing a novel method, i.e., concolic walking along the event-dependency graph. We implement AppWalker based on it. Evaluation over a real-life app set shows that better efficiency and accuracy than state-of-the-art concolic analysis tools are achieved.
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Notes
- 1.
Acteve, benchmark, and corresponding coverage measurement tools are all taken from the AndroidTest [13] project.
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Acknowledgments
The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped to improve the quality of this paper. This work was supported by the National Natural Science Foundation of China under Grants Nos. 61170286, 61202486.
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Wu, T., Yang, Y. (2016). AppWalker: Efficient and Accurate Dynamic Analysis of Apps via Concolic Walking Along the Event-Dependency Graph. In: Wang, G., Ray, I., Alcaraz Calero, J., Thampi, S. (eds) Security, Privacy and Anonymity in Computation, Communication and Storage. SpaCCS 2016. Lecture Notes in Computer Science(), vol 10067. Springer, Cham. https://doi.org/10.1007/978-3-319-49145-5_9
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DOI: https://doi.org/10.1007/978-3-319-49145-5_9
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