南京理工大学学报（自然科学版）2016，Vol.40Issue(1)：35-40,6.DOI:10.14177/j.cnki.32-1397n.2016.40.01.006
基于集成分类的恶意应用检测方法
Mobile malware detection approach using ensemble classification
黄伟 1陈昊 1郭雅娟 1姜海涛1
作者信息
- 1. 江苏省电力公司 电力科学研究院,江苏 南京210036
- 折叠
摘要
Abstract
To accurately know the contributions of a single feature and a single data mining algorithm to high detection accuracy for malware detection,this paper puts forward a mobile malware detection approach using ensemble techniques for the Android platform. The proposed approach extracts three kinds of features from a given mobile application,including privilege feature,component feature and API call feature. Several classification models are built for each kind of feature using several base classifiers respectively. A consensus function for each feature is designed to make decision to obtain an optimal classification output. In the next step,another consensus function is designed and applied to the outputs from all kinds of features in order to obtain the final classification output. This paper carries out the empirical experiment evaluation on mobile applications from the real world application markets,and the compared results show that our approach can get a better detection accuracy in terms of F1 score than a single data mining algorithm.关键词
安卓/分类/集成学习/恶意应用检测/静态分析/支持向量机/特征选择Key words
Android/classification/ensemble learning/malware detection/static analysis/support vector machine/feature selection分类
信息技术与安全科学引用本文复制引用
黄伟,陈昊,郭雅娟,姜海涛..基于集成分类的恶意应用检测方法[J].南京理工大学学报（自然科学版）,2016,40(1):35-40,6.
