Research on Android malware detection based on permission and behavior

Xiao-min ZHANG; Jing LUI; Jun-xi ZHUANG; Ying-xu LAI

doi:10.11959/j.issn.2096-109x.2017.00159

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Research on Android malware detection based on permission and behavior

academic paper | 更新时间：2024-06-03

- Research on Android malware detection based on permission and behavior
- Chinese Journal of Network and Information Security Vol. 3, Issue 3, Pages: 51-57(2017)
- 作者机构：
  
  1. 北京工业大学信息学部，北京 100124
  2. 北京工业大学可信计算北京市重点实验室，北京 100124
  3. 北京工业大学信息安全等级保护关键技术国家工程实验室，北京 100124
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2017.00159
  CLC： TP309
- Online First：2017-03，
  
  Published：15 March 2017
- 稿件说明：
移动端阅览
Xiao-min ZHANG, Jing LUI, Jun-xi ZHUANG, et al. Research on Android malware detection based on permission and behavior[J]. Chinese Journal of Network and Information Security, 2017, 3(3): 51-57.
DOI：

Xiao-min ZHANG, Jing LUI, Jun-xi ZHUANG, et al. Research on Android malware detection based on permission and behavior[J]. Chinese Journal of Network and Information Security, 2017, 3(3): 51-57. DOI： 10.11959/j.issn.2096-109x.2017.00159.

摘要

针对Android平台恶意应用，从Android自身权限机制入手，提出了一种静态权限特征分析和动态行为分析相结合、将行为映射为权限特征的方法，并采用关联分析算法挖掘出权限特征之间的关联规则，把权限特征和行为特征作为朴素贝叶斯分类算法的输入，建立了一个恶意应用检测模型，最后通过实验验证了该方法的有效性和准确性。

Abstract

For the Android platform malicious application

a method of mapping behavior to privilege characteristics by combining static privilege feature analysis and dynamic behavior analysis was proposed

and association analysis algorithm was used to dig out the association rules between privilege features.Feature as a naive Bayesian classification algorithm input

a malicious application detection model was established.Finally

the experiment verify the effectiveness and accuracy of the method.

关键词

Keywords

references

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