DBN和GRU混合的Android恶意软件检测模型被引量：2

Android Malware Detection Model Using Combined DBN and GRU

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摘要针对Android平台恶意软件数量增长迅猛,种类日益增多的现状,提出了一种基于深度置信网络和门控循环单元网络混合的Android恶意软件检测模型。通过自动化提取Android应用软件的特征,包括权限等静态特征和应用运行时的动态特征进行训练,对Android恶意软件进行检测和分类。实验结果表明,混合了门控循环单元网络和深度置信网络的混合模型,在检测效果上优于传统的机器学习算法和深度置信网络模型。 With the rapid growth of the number and variety of malwares on the Android platform,an Android malware detection model based on a mixture of deep belief networks and gated recurrent unit was proposed.The android malware is detected and classified by automatically extracting the features of Android application software,including static features such as permissions and dynamic features during application runtime.Experimental results show that the hybrid model with a gated recurrent unit network is superior to traditional machine learning algorithms and deep belief network models in detecting results.

作者欧阳立芦天亮暴雨轩李默 OUYANG Li;LU Tianliang;BAO Yuxuan;LI Mo(School of Police Information Engineering and Cyber Security,People s Public Security University of China,Beijing 100076,China)

机构地区中国人民公安大学警务信息工程与网络安全学院

出处《中国人民公安大学学报（自然科学版）》 2020年第1期104-108,共5页 Journal of People’s Public Security University of China(Science and Technology)

基金国家重点研发计划“网络空间安全”重点专项(2017YFB0802804) “十三五”国家密码发展基金密码理论研究重点课题(MMJJ20180108)。

关键词安卓恶意软件检测深度置信网络门控循环单元 Android malware detection deep belief network gated recurrent unit

分类号 TP393.08 [自动化与计算机技术—计算机应用技术]

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参考文献3

1Zhenlong Yuan,Yongqiang Lu,Yibo Xue.Droid Detector:Android Malware Characterization and Detection Using Deep Learning[J].Tsinghua Science and Technology,2016,21(1):114-123. 被引量：37
2苏志达,祝跃飞,刘龙.基于深度学习的安卓恶意应用检测[J].计算机应用,2017,37(6):1650-1656. 被引量：21
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