计算机应用研究2017，Vol.34Issue(7)：2184-2188,5.DOI:10.3969/j.issn.1001-3695.2017.07.058
基于最小距离分类器的Android恶意软件检测方案
Android malware detection based on minimum-distance classifier
何文才 1闫翔宇 2刘培鹤 1刘畅2
作者信息
- 1. 西安电子科技大学 通信工程学院,西安 710071
- 2. 北京电子科技学院 通信工程系,北京 100070
- 折叠
摘要
Abstract
Considering the problem of the increasing number of android malware software and the increasing difficulty of large-scale software security detection,this paper proposed a lightweight malware detection method.Firstly,it analyzed plenty of Android software permission frequencies information.Secondly,it utilized permission frequencies to reduce redundancy.Finally it used minimum-distance classifier to classify the software.The experiments result proves that it is feasible.By comparison,it performs better in complexity and detection accuracy than other related work,which can be applied to preliminary analysis in large-scale software security detection.关键词
最小距离分类器/安卓/权限频率/恶意软件检测/数据挖掘Key words
minimum-distance classifier/Android/permission frequencies/malware application detection/data mining分类
信息技术与安全科学引用本文复制引用
何文才,闫翔宇,刘培鹤,刘畅..基于最小距离分类器的Android恶意软件检测方案[J].计算机应用研究,2017,34(7):2184-2188,5.
