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基于深度学习的Android恶意软件检测:成果与挑战

陈怡 唐迪 邹维

陈怡, 唐迪, 邹维. 基于深度学习的Android恶意软件检测:成果与挑战[J]. 电子与信息学报, 2020, 42(9): 2082-2094. doi: 10.11999/JEIT200009
引用本文: 陈怡, 唐迪, 邹维. 基于深度学习的Android恶意软件检测:成果与挑战[J]. 电子与信息学报, 2020, 42(9): 2082-2094. doi: 10.11999/JEIT200009
Yi CHEN, Di TANG, Wei ZOU. Android Malware Detection Based on Deep Learning: Achievements and Challenges[J]. Journal of Electronics and Information Technology, 2020, 42(9): 2082-2094. doi: 10.11999/JEIT200009
Citation: Yi CHEN, Di TANG, Wei ZOU. Android Malware Detection Based on Deep Learning: Achievements and Challenges[J]. Journal of Electronics and Information Technology, 2020, 42(9): 2082-2094. doi: 10.11999/JEIT200009

基于深度学习的Android恶意软件检测:成果与挑战

doi: 10.11999/JEIT200009
基金项目: 中国科学院重点实验室基金(CXJJ-19S022)
详细信息
    作者简介:

    陈怡:1991年生,博士生,研究方向为移动应用安全、漏洞挖掘

    唐迪:1991年生,博士生,研究方向为基于机器学习的安全研究

    邹维:1964年生,研究员,博士生导师,研究方向为网络与软件安全

    通讯作者:

    邹维 zouwei@iie.ac.cn

  • 1)百度手机助手:https://shouji.baidu.com2)小米应用商店:http://app.mi.com3)华为应用市场:https://appstore.huawei.com4)VirusTotal:https://www.virustotal.com
  • 5)下载地址:http://R2D2.TWMAN.ORG
  • 6)表3表4表5中,加粗条目表示该指标下最优异的测试结果。
  • 中图分类号: TP309.5

Android Malware Detection Based on Deep Learning: Achievements and Challenges

Funds: Foundation of Key Laboratory of Network Assessment Technology, Chinese Academy of Sciences (CXJJ-19S022)
  • 摘要: 随着Android应用的广泛使用,Android恶意软件数量迅速增长,对用户的财产、隐私等造成的安全威胁越来越严重。近年来基于深度学习的Android恶意软件检测成为了当前安全领域的研究热点。该文分别从数据采集、应用特征、网络结构、效果检测4个方面,对该研究方向已有的学术成果进行了分析与总结,讨论了它们的局限性与所面临的挑战,并就该方向未来的研究重点进行了展望。
    注释:
    1)  1)百度手机助手:https://shouji.baidu.com2)小米应用商店:http://app.mi.com3)华为应用市场:https://appstore.huawei.com4)VirusTotal:https://www.virustotal.com
    2)  5)下载地址:http://R2D2.TWMAN.ORG
    3)  6)表3表4表5中,加粗条目表示该指标下最优异的测试结果。
  • 表  1  Android恶意软件公开数据集统计表

    数据集名称恶意软件数量软件收集时间软件检测方法下载链接
    VirusShare[24]343118792011至今未说明https://virusshare.com
    AndroZoo[25]13029682011至今VirusTotalhttps://androzoo.uni.lu
    ArgusLab[26]246502010~2016VirusTotalhttp://amd.arguslab.org
    Drebin[28]55602010~2012VirusTotalhttp://contagiominidump.blogspot.com
    ISCX[29]19292012~2015VirusTotalhttps://www.unb.ca/cic/datasets/index.html
    Genome[30]12602010~2011未说明http://www.malgenomeproject.org
    Contagio[27]2522011~2018未说明http://contagiominidump.blogspot.com
    下载: 导出CSV

    表  2  公司合作及数据采集统计表

    文献合作公司良性软件恶意软件
    文献[31]腾讯安全实验室83784106912
    文献[32]McAfee1962011505
    文献[20]McAfee36272475
    文献[33]Comodo25002500
    文献[34]Comodo15001500
    文献[35]Leopard Mobile Inc2000000
    下载: 导出CSV

    表  3  在相同数据下现有深度学习模型与传统机器学习模型效果对比统计表(%)

    研究工作评价指标深度学习模型传统机器学习模型
    支持向量机决策树朴素贝叶斯逻辑回归随机森林K最近邻
    文献[12]m496.580.077.579.078.0
    文献[14]m110053.347.0
    m298.334.854.0
    m499.466.082.0
    文献[19]m195.7792.0875.0979.2264.18
    m297.8493.7598.6491.8295.91
    m496.7692.8482.9583.8671.19
    文献[22]m199.5294.2393.7795.6497.0495.40
    m299.8395.8994.6895.9094.6993.16
    m399.7495.0594.2295.7795.8594.27
    m499.6894.9794.1395.8295.9394.29
    文献[32]m194.8287.69276.593.8
    m297.7687.59276.893.8
    m590.8694.495.585.597.1
    m69.145.64.514.52.9
    m72.2424.213.93812
    注:各评价指标的含义如下。m1:精确率(Precision),m2:召回率/真正率(recall/TPR),m3:F-measure,m4:准确率(accuracy),m6:假正率(FPR),m7:假负率(FNR)
    下载: 导出CSV

    表  4  在不同数据不同特征下现有基于深度学习的方法与基于传统机器学习的方法效果对比统计表

    研究工作机器学习模型m1(%)m2(%)m3(%)m4(%)m6(%)m7(%)m8(s)
    文献[11]深度学习9899
    文献[28]支持向量机93.9
    文献[62]决策树78
    文献[63]朴素贝叶斯93
    文献[61]K最近邻99
    文献[64]极限梯度提升决策树9797
    文献[35]深度学习969390.5
    文献[28]支持向量机94.01.00.75
    文献[65]随机森林95.3920.3419.8
    文献[39]深度学习98.8498.4798.6598.86
    文献[66]逻辑回归80.9987.1183.9383.26
    文献[44]深度学习98.981.58
    文献[67]随机森林97.424.33
    文献[20]深度学习99959798
    文献[68]支持向量机98
    文献[69]朴素贝叶斯94919291
    文献[67]随机森林98979797
    注:各评价指标的含义如下。m1:精确率(Precision),m2:召回率/真正率(recall/TPR),m3:F-measure,m4:准确率(accuracy),m6:假正率(FPR),m7:假负率(FNR),m8:检测时间
    下载: 导出CSV

    表  5  基于深度学习的Android恶意软件检测工作效果互相对比统计表(%)

    研究工作m1m2m3m4m6m7
    文献[11]9998
    文献[19]96.8
    文献[20]8687
    文献[35]969390.5
    文献[20]99.39932.5
    文献[39]98.8798.4798.6598.86
    文献[13]83.2487.6785.3984.95
    文献[18]94.7691.3193.0093.10
    文献[20]6798.4771.0069.00
    文献[44]98.981.58
    文献[20]89.506.72
    文献[32]98.0999.5698.8298.5
    文献[33]93.9693.3693.6893.68
    文献[19]96.7896.7696.7696.76
    文献[20]99959798
    文献[21]95.31
    文献[35]93
    文献[33]93.68
    注:各评价指标的含义如下。m1:精确率(Precision),m2:召回率/真正率(recall/TPR),m3:F-measure,m4:准确率(accuracy),m6:假正率(FPR),m7:假负率(FNR)
    下载: 导出CSV
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    出版历程
    • 收稿日期:  2020-01-20
    • 修回日期:  2020-07-30
    • 网络出版日期:  2020-08-07
    • 刊出日期:  2020-09-27

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