Abstract
In the Android system security issue, the maliciousness of the applications is closely related to the permissions they applied. In this paper, a population-based model is proposed for detecting Android malicious application. Which is in the view of the current disadvantages of missing report, long detection period caused by features redundancy, and the instability of detection rate lead by unbalanced data of benign and malicious samples. Drawing on the idea of population in biology, each app was labeled by preprocessing. And adaptive feature vectors were automatically selected through the feature engineering. Thus the malicious application detection is carried out in the form of hybrid model voting. The experimental results show that feature engineering can remove a large amount of redundancy before classification. And the hybrid voting model can provide adaptive detection service for different populations.
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Acknowledgement
Authors are partially supported by Major projects of the Hubei Provincial Education Department (No. 17ZD014), Hubei college students’ Innovation and Entrepreneurship Training Program project (No. 201610488020), National defense pre research fund of Wuhan University of Science and Technology (No. GF201712) and Colleges and Universities in Hubei Provincial College Students’ Innovation Entrepreneurial Training Program (No. 201710488027).
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Xiao, Z., Li, T., Wang, Y. (2019). Using Hybrid Model for Android Malicious Application Detection Based on Population (Short Paper). In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_52
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