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- W2913144412 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." @default.
- W2913144412 created "2019-02-21" @default.
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- W2913144412 date "2019-01-01" @default.
- W2913144412 modified "2023-10-08" @default.
- W2913144412 title "Using Hybrid Model for Android Malicious Application Detection Based on Population (Short Paper)" @default.
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- W2913144412 doi "https://doi.org/10.1007/978-3-030-12981-1_52" @default.
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