Abstract
In recent years, there is a rapid increase in the number of Android based malware. In this paper we propose a malware detection method using bytecode code image. We firstly extract bytecode file from Android APK file, and then convert the bytecode file into an image file. Finally we use convolution neural network (CNN) to classify malware. the proposed method directly convert a bytecode file into an image data, so CNN can automatically learn features of malware, and use the learned features to classify malware. Especially for malware which uses polymorphic techniques to encrypt functional code, the proposed method can detect it without using unpacking tools. The experimental results show it is feasible to detect malware using CNN, especially for detecting encrypted malware.
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Acknowledgements
This work was partially supported by Scientific Research Foundation in Shenzhen (Grant No. JCYJ20160525163756635), Guangdong Natural Science Foundation (Grant No. 2016A030313664) and Key Laboratory of Network Oriented Intelligent Computation (Shenzhen).
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Ding, Y., Wu, R., Xue, F. (2018). Detecting Android Malware Using Bytecode Image. In: Xiao, J., Mao, ZH., Suzumura, T., Zhang, LJ. (eds) Cognitive Computing – ICCC 2018. ICCC 2018. Lecture Notes in Computer Science(), vol 10971. Springer, Cham. https://doi.org/10.1007/978-3-319-94307-7_13
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DOI: https://doi.org/10.1007/978-3-319-94307-7_13
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