Research Article
Using Hybrid Model for Android Malicious Application Detection Based on Population (Short Paper)
@INPROCEEDINGS{10.1007/978-3-030-12981-1_52, author={Zhijie Xiao and Tao Li and Yuqiao Wang}, title={Using Hybrid Model for Android Malicious Application Detection Based on Population (Short Paper)}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings}, proceedings_a={COLLABORATECOM}, year={2019}, month={2}, keywords={Android security Population Feature engineering Security detection}, doi={10.1007/978-3-030-12981-1_52} }- Zhijie Xiao
Tao Li
Yuqiao Wang
Year: 2019
Using Hybrid Model for Android Malicious Application Detection Based on Population (Short Paper)
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-12981-1_52
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.