计算机应用与软件2017，Vol.34Issue(1)：286-292,7.DOI:10.3969/j.issn.1000-386x.2017.01.052
基于关联特征的贝叶斯Android恶意程序检测技术
BAYESIAN ANDROID MALWARE DETECTION TECHNOLOGY BASED ON THE FEATURES OF ASSOCIATION
摘要
Abstract
There is a close relationship between the Android malware and the application's permissions, in view of the detection rate is not high of current detection technology, the existence of false positives, and lack of detection of unknown malicious.A static detection method based on the characteristics of associated permissions is proposed to realize the effective detection of Android malware.First of all, the characteristics of the application permissions are preprocessed, and the permissions association dataset is constructed by the frequent pattern mining algorithm, then the redundancy feature selection algorithm is designed to simplify the redundancy, finally the feature selection is carried out by Mutual information, independent feature spaces with the most ability to classify.The experimental results show that dealing with features has a better validity and reliability before Bayesian classification, the detection rate can be stable in 92.1％, the false positive rate is 8.3％, the detection accuracy rate is 93.7％.关键词
贝叶斯分类/安卓/恶意检测/关联特征/特征选择Key words
Bayesian classification/Android/Malware detection/Associate features/Feature selection分类
信息技术与安全科学引用本文复制引用
王聪,张仁斌,李钢..基于关联特征的贝叶斯Android恶意程序检测技术[J].计算机应用与软件,2017,34(1):286-292,7.基金项目
国家自然科学基金项目(61273237). （61273237）
