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

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

陈怡^{1, 3, 4},
唐迪²,
邹维^{1, 3, 4, ,}

1.
中国科学院信息工程研究所北京 100093
2.
香港中文大学香港 999077
3.
中国科学院网络测评技术重点实验室北京 100093
4.
中国科学院大学网络空间安全学院北京 100049

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⁾在表3、表4和表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
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- HTML全文浏览量: 817
- PDF下载量: 256
- 被引次数: 0
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

Android Malware Detection Based on Deep Learning: Achievements and Challenges

Yi CHEN^{1, 3, 4},
Di TANG²,
Wei ZOU^{1, 3, 4
, ,}

1.
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
2.
Chinese University of Hong Kong, Hongkong 999077, China
3.
Key Laboratory of Network Assessment Technology, Chinese Academy of Sciences, Beijing 100093, China
4.
School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049, China

Funds: Foundation of Key Laboratory of Network Assessment Technology, Chinese Academy of Sciences (CXJJ-19S022)

摘要

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。
- 移动安全 /
- Android恶意软件 /
- Android应用 /
- 深度学习 /
- 机器学习
Abstract: With the prosperous of Android applications, Android malware has been scattered everywhere, which raises the serious security risk to users. On the other hand, the rapid developing of deep learning fires the combat between the two sides of malware detection. Inducing deep learning technologies into Android malware detection becomes the hottest topic of society. This paper summarizes the existing achievements of malware detection from four aspects: Data collection, feature construction, network structure and detection performance. Finally, the current limitations and facing challenges followed by the future researches are discussed.
- Mobile security /
- Android malware /
- Android application /
- Deep learning /
- Machine learning
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⁾在表3、表4和表5中，加粗条目表示该指标下最优异的测试结果。

HTML全文

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]	34311879	2011至今	未说明	https://virusshare.com
AndroZoo^[25]	1302968	2011至今	VirusTotal	https://androzoo.uni.lu
ArgusLab^[26]	24650	2010～2016	VirusTotal	http://amd.arguslab.org
Drebin^[28]	5560	2010～2012	VirusTotal	http://contagiominidump.blogspot.com
ISCX^[29]	1929	2012～2015	VirusTotal	https://www.unb.ca/cic/datasets/index.html
Genome^[30]	1260	2010～2011	未说明	http://www.malgenomeproject.org
Contagio^[27]	252	2011～2018	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	83784	106912
文献[32]	McAfee	19620	11505
文献[20]	McAfee	3627	2475
文献[33]	Comodo	2500	2500
文献[34]	Comodo	1500	1500
文献[35]	Leopard Mobile Inc	2000000

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
研究工作	评价指标	深度学习模型	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]	m4	96.5	80.0	77.5	79.0	78.0
文献[14]	m1	100	53.3		47.0
	m2	98.3	34.8		54.0
	m4	99.4	66.0		82.0
文献[19]	m1	95.77	92.08	75.09	79.22	64.18
	m2	97.84	93.75	98.64	91.82	95.91
	m4	96.76	92.84	82.95	83.86	71.19
文献[22]	m1	99.52	94.23	93.77		95.64	97.04	95.40
	m2	99.83	95.89	94.68		95.90	94.69	93.16
	m3	99.74	95.05	94.22		95.77	95.85	94.27
	m4	99.68	94.97	94.13		95.82	95.93	94.29
文献[32]	m1	94.82	87.6	92	76.5		93.8
	m2	97.76	87.5	92	76.8		93.8
	m5	90.86	94.4	95.5	85.5		97.1
	m6	9.14	5.6	4.5	14.5		2.9
	m7	2.24	24.2	13.9	38		12
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)

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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			98	99
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			97	97
文献[35]	深度学习		96		93	9		0.5
文献[28]	支持向量机		94.0			1.0		0.75
文献[65]	随机森林		95.3		92	0.34		19.8
文献[39]	深度学习	98.84	98.47	98.65	98.86
文献[66]	逻辑回归	80.99	87.11	83.93	83.26
文献[44]	深度学习			98.98		1.58
文献[67]	随机森林			97.42		4.33
文献[20]	深度学习	99	95	97	98
文献[68]	支持向量机			98
文献[69]	朴素贝叶斯	94	91	92	91
文献[67]	随机森林	98	97	97	97
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	m1	m2	m3	m4	m6	m7
文献[11]			99	98
文献[19]				96.8
文献[20]			86	87
文献[35]		96		93	9	0.5
文献[20]		99.3		99	3	2.5
文献[39]	98.87	98.47	98.65	98.86
文献[13]	83.24	87.67	85.39	84.95
文献[18]	94.76	91.31	93.00	93.10
文献[20]	67	98.47	71.00	69.00
文献[44]			98.98		1.58
文献[20]			89.50		6.72
文献[32]	98.09	99.56	98.82	98.5
文献[33]	93.96	93.36	93.68	93.68
文献[19]	96.78	96.76	96.76	96.76
文献[20]	99	95	97	98
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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