Android malware detection based on improved random forest

Hong-yu YANG; Jin XU

doi:10.11959/j.issn.1000-436x.2017073

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Android malware detection based on improved random forest

Papers | 更新时间：2024-06-05

- Android malware detection based on improved random forest
- Journal on Communications Vol. 38, Issue 4, Pages: 8-16(2017)
- 作者机构：
  
  中国民航大学计算机科学与技术学院，天津 300300
- 作者简介：
- 基金信息：
  
  The National Science and Technology Major Project(2012ZX03002002);The Science ＆ Technology Project of CAAC(MHRD201009);The Science ＆ Technology Project of CAAC(MHRD201205)
- DOI：10.11959/j.issn.1000-436x.2017073
  CLC： TP309
- Online First：2017-04，
  
  Published：25 April 2017
- 稿件说明：
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Hong-yu YANG, Jin XU. Android malware detection based on improved random forest[J]. Journal on Communications, 2017, 38(4): 8-16.
DOI：

Hong-yu YANG, Jin XU. Android malware detection based on improved random forest[J]. Journal on Communications, 2017, 38(4): 8-16. DOI： 10.11959/j.issn.1000-436x.2017073.

摘要

针对随机森林（RF

random forest）算法的投票原则无法区分强分类器与弱分类器差异的缺陷，提出一种加权投票改进方法，在此基础上，提出一种检测 Android 恶意软件的改进随机森林分类模型（IRFCM

improved random forest classification model）。IRFCM选取AndroidManifest.xml文件中的Permission信息和Intent信息作为特征属性并进行优化选择，然后应用该模型对最终生成的特征向量进行检测分类。Weka 环境下的实验结果表明IRFCM具有较好的分类精度和分类效率。

Abstract

Aiming at the defect of vote principle in random forest algorithm which is incapable of distinguishing the differences between strong classifier and weak classifier

a weighted voting improved method was proposed

and an improved random forest classification (IRFCM) was proposed to detect Android malware on the basis of this method.The IRFCM chose Permission information and Intent information as attribute features from AndroidManifest.xml files and optimized them

then applied the model to classify the final feature vectors.The experimental results in Weka environment show that IRFCM has better classification accuracy and classification efficiency.

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Keywords

references

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