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Using Hybrid Model for Android Malicious Application Detection Based on Population (Short Paper)

  • Zhijie Xiao
  • Tao LiEmail author
  • Yuqiao Wang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)

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.

Keywords

Android security Population Feature engineering Security detection 

Notes

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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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial SystemWuhanChina

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