计算机应用研究2017，Vol.34Issue(6)：1774-1777,1782,5.DOI:10.3969/j.issn.1001-3695.2017.06.038
基于机器学习算法的Android恶意程序检测系统
Malware detection system implementation of Android application based on machine learning
张家旺 1李燕伟1
作者信息
- 1. 国家计算机网络应急技术处理协调中心实验室,北京100029
- 折叠
摘要
Abstract
For the weakness of traditional malware detection methods,this paper proposed a method in the detection of unknown malicious applications based data mining and machine learning algorithm.While a single feature of machine learning algorithms could not play the role of ability of data processing,detection effect.This paper proposed a method to combine speech recognition model with random forest algorithm,which considered multi-class APK features in unknown malware detection.First,it combined a variety of ways to extract 3 classes which could reflect the behaviors of Android malware including sensitive permissions,DVM function calls and OpCodes characteristics.Then,according to the characteristics of each type of Ngram model,each one could evaluate behaviors of malware independently.Finally,3 classes of feature model would join into a random forest learning algorithm,so as to detect the Android apps.It implemented an automated system based on this method to detect 811 non-malicious and 826 malicious apps with higher accuracy.Considering comprehensive evaluation of various indicators,the experimental results show that the malware detection system has a better performance than other related works on effective and accuracy.关键词
随机森林/恶意代码检测/多类特征/安卓应用/机器学习Key words
random forest/malicious code detection/multiple feature/Android application/machine learning分类
信息技术与安全科学引用本文复制引用
张家旺,李燕伟..基于机器学习算法的Android恶意程序检测系统[J].计算机应用研究,2017,34(6):1774-1777,1782,5.
