Abstract
Traditional machine learning based malware detection methods often use decompiling techniques or dynamic monitoring techniques to extract the feature representation of malware. This procedure is time consuming and strongly depends on the skills of experts. In addition, malware can be packed or encrypted to evade the analysis of decompiling tools. To solve this issue, we propose a static detection method based on deep learning. We directly extract bytecode file from Android APK file, and convert the bytecode file into a two-dimensional bytecode matrix, then use the deep learning algorithm, convolution neural network (CNN), to train a detection model and apply it to classify malware. CNN can automatically learn features of bytecode file which can be used to recognize malware. The proposed detection model avoids the procedure for analyzing malware features and designing the feature representation of malware. The experimental results show the proposed method is effective to detect malware, especially malware encrypted using polymorphic techniques.
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Acknowledgements
This research was funded by Scientific Research Foundation in Shenzhen, Grant numbers JCYJ20180525163756635 and JCYJ20180507183608379, Guangdong Natural Science Foundation, Grant number 2016A030313664, the National Key R&D Program of China under (Grant nos. 2018YFB1003800, 2018YFB1003805), and the National Natural Science Foundation of China (Grant no. 61872107).
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Ding, Y., Zhang, X., Hu, J. et al. Android malware detection method based on bytecode image. J Ambient Intell Human Comput 14, 6401–6410 (2023). https://doi.org/10.1007/s12652-020-02196-4
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DOI: https://doi.org/10.1007/s12652-020-02196-4