Draw-a-Deep Pattern: Drawing Pattern-Based Smartphone User Authentication Based on Temporal Convolutional Neural Network

被引:5
作者
Kim, Junhong [1 ]
Kang, Pilsung [1 ]
机构
[1] Korea Univ, Sch Ind & Management Engn, Seoul 02841, South Korea
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 15期
基金
新加坡国家研究基金会;
关键词
mobile user authentication; behavioral biometrics; temporal convolution neural network; recurrent neural network; sequence modeling; BIOMETRICS; SECURITY;
D O I
10.3390/app12157590
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Present-day smartphones provide various conveniences, owing to high-end hardware specifications and advanced network technology. Consequently, people rely heavily on smartphones for a myriad of daily-life tasks, such as work scheduling, financial transactions, and social networking, which require a strong and robust user authentication mechanism to protect personal data and privacy. In this study, we propose draw-a-deep-pattern (DDP)-a deep learning-based end-to-end smartphone user authentication method using sequential data obtained from drawing a character or freestyle pattern on the smartphone touchscreen. In our model, a recurrent neural network (RNN) and a temporal convolution neural network (TCN), both of which are specialized in sequential data processing, are employed. The main advantages of the proposed DDP are (1) it is robust to the threats to which current authentication systems are vulnerable, e.g., shoulder surfing attack and smudge attack, and (2) it requires few parameters for training; therefore, the model can be consistently updated in real-time, whenever new training data are available. To verify the performance of the DDP model, we collected data from 40 participants in one of the most unfavorable environments possible, wherein all potential intruders know how the authorized users draw the characters or symbols (shape, direction, stroke, etc.) of the drawing pattern used for authentication. Of the two proposed DDP models, the TCN-based model yielded excellent authentication performance with average values of 0.99%, 1.41%, and 1.23% in terms of AUROC, FAR, and FRR, respectively. Furthermore, this model exhibited improved authentication performance and higher computational efficiency than the RNN-based model in most cases. To contribute to the research/industrial communities, we made our dataset publicly available, thereby allowing anyone studying or developing a behavioral biometric-based user authentication system to use our data without any restrictions.
引用
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页数:21
相关论文
共 39 条
[1]   Spin-lock Gesture Authentication for Mobile Devices [J].
Aly, Yomna ;
Munteanu, Cosmin ;
Raimondo, Stefania ;
Wu, Alan Yusheng ;
Wei, Molly .
PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON HUMAN-COMPUTER INTERACTION WITH MOBILE DEVICES AND SERVICES (MOBILEHCI 2016), 2016, :775-782
[2]   A study on usability and security features of the Android pattern lock screen [J].
Andriotis, Panagiotis ;
Oikonomou, George ;
Mylonas, Alexios ;
Tryfonas, Theo .
INFORMATION AND COMPUTER SECURITY, 2016, 24 (01) :53-72
[3]  
Aviv A.J., 2010, P 4 USENIX C OFF TEC, P1
[4]  
Ba Jimmy Lei, 2016, P ADV NEUR INF PROC
[5]  
Bai S., 2018, ARXIV
[6]  
Corpus KR, 2016, 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON MOBILE SOFTWARE ENGINEERING AND SYSTEMS (MOBILESOFT 2016), P11, DOI [10.1109/MobileSoft.2016.015, 10.1145/2897073.2897111]
[7]  
De Luca A., Proceedings of the 2012 AC M annual conference on Human Factors in Computing Systems, ser. CHI '12. New York, NY, USA: ACM, P987, DOI [10.1145/2208516.2208544, DOI 10.1145/2208516.2208544]
[8]   COMPARING THE AREAS UNDER 2 OR MORE CORRELATED RECEIVER OPERATING CHARACTERISTIC CURVES - A NONPARAMETRIC APPROACH [J].
DELONG, ER ;
DELONG, DM ;
CLARKEPEARSON, DI .
BIOMETRICS, 1988, 44 (03) :837-845
[9]  
Dunphy P, 2007, CCS'07: PROCEEDINGS OF THE 14TH ACM CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P36
[10]  
Feng T, 2012, 2012 IEEE INTERNATIONAL CONFERENCE ON TECHNOLOGIES FOR HOMELAND SECURITY, P451, DOI 10.1109/THS.2012.6459891