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Meta-Path and Matrix Factorization Based Shilling Detection for Collaborate Filtering Read chapter Read first chapter

2019 | OriginalPaper | Chapter

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

Authors : Zhijie Xiao, Tao Li, Yuqiao Wang

Published in: Collaborative Computing: Networking, Applications and Worksharing

Publisher: Springer International Publishing

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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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previous chapter The Cuckoo Search and Integer Linear Programming Based Approach to Time-Aware Test Case Prioritization Considering Execution Environment
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Metadata
Title
Using Hybrid Model for Android Malicious Application Detection Based on Population (Short Paper)
Authors
Zhijie Xiao
Tao Li
Yuqiao Wang
Copyright Year
2019
Book
Collaborative Computing: Networking, Applications and Worksharing

Print ISBN: 978-3-030-12980-4

Electronic ISBN: 978-3-030-12981-1

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-12981-1_52