计算机工程与应用2016，Vol.52Issue(20)：5-13,9.DOI:10.3778/j.issn.1002-8331.1605-0171
Android恶意软件的多特征协作决策检测方法
Android malware detection method based on multifeature collaborative decision
摘要
Abstract
With more and more widespread use of smartphones, malwares have become increasingly complex and large-scalely. As a free and open source system, Android has currently surpassed other mobile platforms to become the most popular operating system, so that the number of the Android platform malware has also been significantly increased. Focusing on the security issues of the software for the Android platform, this paper proposes an Android malware detection method based on multifeature collaborative decision. This method mainly bases on the analysis of the Android application, and then the feature attributes are extracted, the models according to machine learning are built. Lastly the classification algorithms are used to determine whether the application is malware. Experimental results show that using the proposed method to classify Android application data set has better assessments indicators than the indicators using other classifiers. Therefore, the method based on multi-feature collaborative decision approach to detect malicious software on Android applications can be made effective for detecting unknown malicious nature of the applications, and can avoid damage caused by malicious applications for the users.关键词
Android平台/恶意软件/多特征协作决策/机器学习Key words
Android platform/malware/multifeature collaborative decision/machine learning分类
信息技术与安全科学引用本文复制引用
魏理豪,艾解清,邹洪,崔磊,龙震岳..Android恶意软件的多特征协作决策检测方法[J].计算机工程与应用,2016,52(20):5-13,9.基金项目
通信对抗技术国防科技重点实验室基金。 （）
