《计算机应用研究》|Application Research of Computers

基于统计学特征的Android恶意应用检测方法

Android malicious application detection method based on statistical features

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作者 冷波,李建彬
机构 中南大学 a.信息科学与工程学院;b.信息安全与大数据研究院,长沙 410083
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文章编号 1001-3695(2019)08-048-2469-04
DOI 10.19734/j.issn.1001-3695.2018.03.0173
摘要 针对Android恶意应用检测中忽略特征统计学意义的问题,提出一种基于统计学特征的Android恶意应用检测方法。该方法提取应用统计学特征作为训练数据集,并采用聚类算法预处理恶意数据集以降低个体差异性对实验结果的影响;另一方面,该方法结合特征和多种机器学习算法(如线性回归、神经网络等)建立了检测模型,提出的两个模型准确率均能达到95%以上,检测时间相比于对比实验也能大幅度降低。实验结果表明,应用的统计学特征能够很好地区分良性和恶意应用,并且通过聚类算法预处理数据能够提高检测精度。
关键词 统计学特征; 机器学习; 个体差异性; 恶意应用检测
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本文URL http://www.arocmag.com/article/01-2019-08-048.html
英文标题 Android malicious application detection method based on statistical features
作者英文名 Leng Bo, Li Jianbin
机构英文名 a.School of Information Science & Engineering,b.Information Security & Big Data Research Institute,Central South University,Changsha 410083,China
英文摘要 Aiming at the problem of ignoring the statistical significance of features in detection of Android malicious applications, this paper proposed an Android malicious application detection method based on statistical features. This method extracted the statistical characteristics of the training data set and used a clustering algorithm to preprocess the malicious data set for reducing the impact of individual differences on the experimental results. On the other hand, this method combined the features and various machine learning algorithms(such as linear regression, neural network, etc. ) to establish a detection model. The accuracy rate of the two models established by this method could reach more than 95%, and the detection time could be greatly reduced compared with the comparison experiment. Experimental results show that the statistical characteristics of the application can be used to distinguish between benign and malicious applications, and preprocessing the data by clustering algorithm can improve the detection accuracy.
英文关键词 statistical features; machine learning; individual difference; malware detection
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收稿日期 2018/3/8
修回日期 2018/4/25
页码 2469-2472
中图分类号 TP309.2
文献标志码 A