Android恶意软件静态检测模型
杨宏宇;徐晋
【期刊名称】《吉林大学学报（工学版）》 【年(卷),期】2018(048)002
【摘要】为解决Android恶意应用泛滥的问题,提出一种Android恶意应用静态检测模型.模型选取AndroidManifest.xml文件中3个标签项属性值作为特征属性,采用信息增益(IG)算法对特征属性进行优化选择,根据优化结果生成对应特征向量集合.最后,应用知识分析Waikato环境(WEKA)的4种机器学习分类算法对特征向量集合进行检测和分类.实验结果表明,本文提出的静态检测模型具有较好的检测分类效果.%In order to solve the problem of Android malware spreading,this paper proposes an Android malware static detection model.First,the attribute value of three label items is selected in AndroidManifest.xml file as the feature attribute.Second,the Information Gain algorithm (IG) is used to optimize the feature attribute,then a corresponding feature vector set is generated according to the optimization results.Finally,four kinds of machine learning classification algorithms in Waikato Environment for Knowledge Analysis (WEKA) are used to detect and classify the feature vector set.Experiment results demonstrate that the proposed static detection method has better detection and classification performance. 【总页数】7页(564-570)
【关键词】计算机应用;Android;恶意软件;静态检测;属性;分类
Android恶意软件静态检测模型
