Continuous identity authentication of mobile intelligent terminal users via interactive behavior

doi:10.12267/j.issn.2096-5931.2024.01.004

Abstract

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

CLC Number:

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

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

Figures/Tables 6

References 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%