Abstract
With the rapid development of mobile Internet, mobile devices have been widely used in people’s daily life, which has made mobile platforms a prime target for malware attack. In this paper we study on Android malware detection method. We propose the method how to extract the structural features of android application from its function call graph, and then use the structure features to build classifier to classify malware. The experiment results show that structural features can effectively improve the performance of malware detection methods.
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Acknowledgment
This work was partially supported by Scientific Research Foundation in Shenzhen (Grant No. JCYJ20140627163809422, JCYJ20160525163756635), Guangdong Natural Science Foundation (Grant No. 2016A030313664) and Key Laboratory of Network Oriented Intelligent Computation (Shenzhen).
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Ding, Y., Zhu, S., Xia, X. (2016). Android Malware Detection Method Based on Function Call Graphs. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_9
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DOI: https://doi.org/10.1007/978-3-319-46681-1_9
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