Abstract
With the rapid development of mobile internet, Android has become the most widely used mobile terminal operating system and play an increasingly important role in users’ lives. However, Android malware is also bringing privacy leaks and security threats that are causing troubles to third-party markets and users. What’s more, malware uses code obfuscation and camouflage to hide itself to avoid detection. Traditional malware detection techniques based on machine learning and feature matching are usually difficult to deal with this type of malware. Considering about this problem, an Android malware detection method based on app-image conversion is proposed, which maps the Android installation files to grayscale images, and employs the deep learning algorithm, CNN (Convolutional Neural Networks), for malware detection. A detection framework for Android malware is presented, which includes three parts: data set construction, app-image conversion, and deep learning detection. In the experiments, the parameters of CNN are determined through comparative analysis. It achieves the detection accuracy of 95.23%, which shows the effectiveness and feasibility of the proposed method.
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Acknowledgments
This work was supported in part by the 13th Five-Year Science and Technology Research Project of the Education Department of Jilin Province under Grant No. JJKH20200794KJ, the Innovation Fund of Changchun University of Science and Technology under Grant No. XJJLG-2018-09, the fund of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education (Jilin University) under Grant No. 93K172018K05.
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Xie, N., Bai, H., Shi, Y., Wu, H. (2021). Android Malware Detection Method Based on App-Image Conversion. In: Tian, Y., Ma, T., Khan, M.K. (eds) Big Data and Security. ICBDS 2020. Communications in Computer and Information Science, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-3150-4_6
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DOI: https://doi.org/10.1007/978-981-16-3150-4_6
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