基于移动智能终端交互行为的持续身份认证技术综述*

doi:10.12267/j.issn.2096-5931.2024.01.004

信息通信技术与政策 ›› 2024, Vol. 50 ›› Issue (1): 19-31.doi: 10.12267/j.issn.2096-5931.2024.01.004

基于移动智能终端交互行为的持续身份认证技术综述^*

Continuous identity authentication of mobile intelligent terminal users via interactive behavior

宋天乐¹, 蔺琛皓¹, 高书馨¹, 赵竣毅², 周亚杰¹, 纪英帅³, 杨明慧⁴, 沈超¹

1.西安交通大学网络空间安全学院,西安 710049
2.西安交通大学信息与通信工程学院,西安 710049
3.西安交通大学自动化科学与工程学院,西安 710049
4.OPPO广东移动通信有限公司,东莞 523860

收稿日期:2023-12-28 出版日期:2024-01-25 发布日期:2024-02-01
通讯作者: 蔺琛皓,西安交通大学网络空间安全学院特聘研究员,长期从事人工智能安全、智能身份安全等方面的研究工作
作者简介:
宋天乐,西安交通大学网络空间安全学院硕士研究生在读,主要从事身份认证、生物特征识别等方面的研究工作
高书馨,西安交通大学网络空间安全学院硕士研究生在读,主要从事可持续身份认证等方面的研究工作
赵竣毅,西安交通大学信息工程学院硕士研究生在读,主要从事身份认证、生物特征识别等方面的研究工作
周亚杰,西安交通大学网络空间安全学院硕士研究生在读,主要从事人机交互行为分析与生成式数据增强等方面的研究工作
纪英帅,西安交通大学自动化科学与工程学院本科生在读,主要从事身份识别方面的研究工作
杨明慧,OPPO广东移动通信有限公司高级软件架构师,长期从事移动终端安全、可信身份识别技术等方面的研究工作
沈超,西安交通大学网络空间安全学院教授,长期从事智能系统安全与控制、可信人工智能与AI安全、软件测试和AI测试、智能人机行为分析等方面的研究工作
基金资助:
*国家自然科学基金项目(62006181);国家自然科学基金项目(62161160337);国家自然科学基金项目(62132011);国家自然科学基金项目(U21B2018);国家自然科学基金项目(U20A201);国家自然科学基金项目(62206217)

SONG Tianle¹, LIN Chenhao¹, GAO Shuxin¹, ZHAO Junyi², ZHOU Yajie¹, JI Yingshuai³, YANG Minghui⁴, SHEN Chao¹

1. School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2. School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
3. School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China
4. Guangdong OPPO Mobile Telecommunications Co., Ltd., Dongguan 523860, China

Received:2023-12-28 Online:2024-01-25 Published:2024-02-01

摘要/Abstract

摘要：

随着移动智能终端的发展和普及,更多用户在智能手机、平板等移动终端上进行支付、转账、存储个人信息等活动。为了提高用户使用移动智能终端的安全性和便利性,目前移动智能终端身份认证的发展趋向于持续认证。首先,回顾了现有基于移动智能终端的身份认证工作;其次,整理了目前主流的性能评估指标和产业界应用;最后,对持续身份认证在移动智能终端中亟待解决的问题以及发展趋势进行了总结和探讨。

关键词: 移动智能终端, 持续认证, 性能评估指标

Abstract:

With the development and popularization of mobile intelligent terminals, more and more users are performing activities such as payment, transfer, and storing personal information on mobile terminals, including smartphones and tablets. In order to improve the security and convenience for users to use mobile intelligent terminals, the current development of mobile intelligent terminal identity authentication tends to be continuous authentication. Firstly, the existing identity authentication work based on mobile intelligent terminal is reviewed. Secondly, the current mainstream performance evaluation indicators and industry applications are organized. Finally, a summary and a discussion are conducted on the urgent issues and development trends of continuous identity authentication in mobile intelligent terminals.

Key words: mobile intelligent terminal, continuous authentication, performance evaluation index

中图分类号:

宋天乐, 蔺琛皓, 高书馨, 赵竣毅, 周亚杰, 纪英帅, 杨明慧, 沈超. 基于移动智能终端交互行为的持续身份认证技术综述^*[J]. 信息通信技术与政策, 2024, 50(1): 19-31.

SONG Tianle, LIN Chenhao, GAO Shuxin, ZHAO Junyi, ZHOU Yajie, JI Yingshuai, YANG Minghui, SHEN Chao. Continuous identity authentication of mobile intelligent terminal users via interactive behavior[J]. Information and Communications Technology and Policy, 2024, 50(1): 19-31.

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链接本文:

http://ictp.caict.ac.cn/CN/10.12267/j.issn.2096-5931.2024.01.004

http://ictp.caict.ac.cn/CN/Y2024/V50/I1/19

图/表 6

参考文献 57

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研究者	采集的数据	模型方法	数据集规模	试验结果
Asadullah等	传感器数据	Distance	10	FAR:1.46%,FRR:6.87%
Lee等	传感器数据	KRR	20	ACC:92%,FAR:7.5%
Li等	传感器数据	SVM	100	FAR:7.65%,FRR:9.01%
Ehatisham等	传感器数据	SVM	10	ACC:97.95%
Li等	传感器数据	SVDD	100	FAR:2.85%,FRR:0.74%
Shen等	传感器数据	HMM	102	FAR:3.98%,FRR:5.03%
Amini等	传感器数据	LSTM	47	ACC:96.7%
Hu等	传感器数据	SVM	100	ACC:87.14%,EER:2.3%
Fereidooni等	传感器数据	CNN	45	FAR:2.3%,FRR:5.7%

研究者	采集的数据	模型方法	数据集规模	试验结果
Asadullah等	传感器数据	Distance	10	FAR:1.46%,FRR:6.87%
Lee等	传感器数据	KRR	20	ACC:92%,FAR:7.5%
Li等	传感器数据	SVM	100	FAR:7.65%,FRR:9.01%
Ehatisham等	传感器数据	SVM	10	ACC:97.95%
Li等	传感器数据	SVDD	100	FAR:2.85%,FRR:0.74%
Shen等	传感器数据	HMM	102	FAR:3.98%,FRR:5.03%
Amini等	传感器数据	LSTM	47	ACC:96.7%
Hu等	传感器数据	SVM	100	ACC:87.14%,EER:2.3%
Fereidooni等	传感器数据	CNN	45	FAR:2.3%,FRR:5.7%

研究者	采集的数据	模型方法	数据集规模	试验结果
Frank等	触屏交互数据	SVM、KNN	20	EER:2%~3%
Antal等	触屏交互数据、传感器数据	Bayes Net、KNN、RF	40	EER:0.2%
Leyfer等	触屏交互数据	RF、Gradient Boosting	14	AUC:96%
Pozo等	触屏交互数据	GMM with UBM	190	EER:15%~22%
Song等	触屏交互数据	FRN	161	EER:2%
Cheng等	触屏交互数据、传感器数据	RF、ET、SVM、KNN	100	ACC:98.3%
Lin等	触屏交互数据、传感器数据	CNN、DNN	1 875	EER:4.38%,AUC:99.2%
Miao等	触屏交互数据、传感器数据	LSTM、DNN	2 100	EER:16.4%

研究者	采集的数据	模型方法	数据集规模	试验结果
Frank等	触屏交互数据	SVM、KNN	20	EER:2%~3%
Antal等	触屏交互数据、传感器数据	Bayes Net、KNN、RF	40	EER:0.2%
Leyfer等	触屏交互数据	RF、Gradient Boosting	14	AUC:96%
Pozo等	触屏交互数据	GMM with UBM	190	EER:15%~22%
Song等	触屏交互数据	FRN	161	EER:2%
Cheng等	触屏交互数据、传感器数据	RF、ET、SVM、KNN	100	ACC:98.3%
Lin等	触屏交互数据、传感器数据	CNN、DNN	1 875	EER:4.38%,AUC:99.2%
Miao等	触屏交互数据、传感器数据	LSTM、DNN	2 100	EER:16.4%

研究者	采集的数据	模型方法	数据集规模	试验结果
Kang等	击键数据	R+A Measure	35	EER:1.9%
Hwang等	击键数据	Distance	25	EER:4%
Inguanez等	击键数据	MLP	32	ACC:94.81%,FAR:6.33%
Buriro等	击键数据、传感器数据	RF	85	TAR:99.35%
Wu等	击键数据、传感器数据	RF	142	ACC:96.85%,FPR:4.01%
Hriez等	击键数据	RF	42	ACC:94.26%

基于移动智能终端交互行为的持续身份认证技术综述^*

Continuous identity authentication of mobile intelligent terminal users via interactive behavior

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研究者	采集的数据	模型方法	数据集规模	试验结果
Wang等	步态数据	KNN	20	EER:3.54%,ACC:87.5%
Gafurov等	步态数据	KNN	21	EER:5.00%,ACC:85.7%
Thang等	步态数据	SVM	11	ACC:92.7%
Hoang等	步态数据	SVM	14	TAR:91.33%
Kork等	步态数据	Distance	50	EER:0.17%~2.28%
Li等	行为分析数据	Rule-based/RBF	42	ACC:94.26%
Cao等	行为分析数据	HMM	26 / 99	EER:30%/16.16%