通信学报2017，Vol.38Issue(4)：8-16,9.DOI:10.11959/j.issn.1000-436x.2017073
基于改进随机森林算法的Android恶意软件检测
Android malware detection based on improved random forest
摘要
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.关键词
随机森林/加权投票/恶意软件/分类检测Key words
random forest/weighted vote/malware/classification detection分类
信息技术与安全科学引用本文复制引用
杨宏宇,徐晋..基于改进随机森林算法的Android恶意软件检测[J].通信学报,2017,38(4):8-16,9.基金项目
国家科技重大专项基金资助项目(No.2012ZX03002002) （No.2012ZX03002002）
中国民航科技基金资助项目(No.MHRD201009,No.MHRD201205)The National Science and Technology Major Project (No.2012ZX03002002),The Science & Technology Project of CAAC (No.MHRD201009,No.MHRD201205) （No.MHRD201009,No.MHRD201205）
