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2020 | OriginalPaper | Buchkapitel

8. Mobile Payment Authentication

verfasst von : Changjun Jiang, Zhong Li

Erschienen in: Mobile Information Service for Networks

Verlag: Springer Singapore

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Abstract

The user’s behavior features are unique and hardly to be imitated, identity authentication based on user behaviors has become a research hotspot. However, none of the existing research has considered the influence of user posture on users’ gesture behavior authentication. In order to make the identity authentication method adapt to the use of the application in different postures, this chapter presents a touch screen behavior authentication system based on user gestures. We collect the user’s gesture behavior data through the touch screen of the mobile phone, collect the user’s posture behavior data through the mobile phone’s orientation sensor and acceleration sensor, and finally extract the user’s posture behavior features and gesture behavior features. In addition, based on this authentication system architecture, we respectively provide two forms of authentication model construction methods: login authentication and continuous authentication. It is possible to monitor from user login to the entire usage process for improving the payment security of mobile devices by using login authentication and continuous authentication comprehensively.

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Vorheriges Kapitel Smart Tourism

J. Changjun, Y. Wangyang, Risk Control Theory of Online Transactions (Science Press, Beijing, 2018)

Y. Meng, D.S. Wong, R. Schlegel et al., Touch gestures based biometric authentication scheme for touchscreen mobile phones, in Proceedings of ICISC, Seoul, Korea (2012), pp. 331–350

Y. Ding, A. Rattani, A. Ross, Bayesian belief models for integrating match scores with liveness and quality measures in a fingerprint verification system, in Proceedings of ICB, Halmstad, Sweden (2016), pp. 1–8

A. Rattani, N. Poh, A. Ross, A Bayesian approach for modeling sensor influence on quality, liveness and match score values in fingerprint verification, in Proceedings of WIFS, Guangzhou, China (2013), pp. 37–42

D. Peralta, M. Galar, I. Triguero et al., A survey on fingerprint minutiae-based local matching for verification and identification. Inf. Sci. 315, 67–87 (2015)

S. Yadav, Fingerprint recognition based on minutiae information. Int. J. Comput. Appl. 120(10), 39–42 (2015)

S. Yang, I. Verbauwhede, Automatic secure fingerprint verification system based on fuzzy vault scheme, in Proceedings of ICASSP, Philadelphia, PA, USA (2005), pp. 609–612

C. Pintavirooj, F.S. Cohen, W. Iampa, Fingerprint verification and identification based on local geometric invariants constructed from minutiae points and augmented with global directional filterbank features. IEICE Trans. Inf. Syst. 97(6), 1599–1613 (2014)CrossRef

Y. Taigman, M. Yang, M. Ranzato et al., DeepFace: closing the gap to human-level performance in face verification, in Proceedings of CVPR, Columbus, Ohio, USA (2014), pp. 1701–1708

10.

O.M. Parkhi, A. Vedaldi, A. Zisserman, Deep face recognition, in Proceeding of BMVC, Swansea, UK (2015), pp. 1–12

11.

F. Schroff, D. Kalenichenko, J. Philbin, FaceNet: a unified embedding for face recognition and clustering, in Proceedings of CVPR, Boston, Massachusetts, USA (2015), pp. 815–823

12.

A. Tefas, C. Kotropoulos, I. Pitas, Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication. IEEE Trans. Pattern Anal. Mach. Intell. 23(7), 735–746 (2001)

13.

D. Li, H. Zhou, K. Lam, High-resolution face verification using pore-scale facial features. IEEE Trans. Image Process. 24(8), 2317–2327 (2015)MathSciNetMATHCrossRef

14.

Y. Sun, X. Wang, X. Tang, Hybrid deep learning for face verification. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 1997–2009 (2016)CrossRef

15.

J. Chen, V.M. Patel, R. Chellappa, Unconstrained face verification using deep CNN features, in Proceedings of WACV, Lake Placid, NY, USA (2016), pp. 1–9

16.

H. Li, L. Zhang, B. Huang et al., Sequential three-way decision and granulation for cost-sensitive face recognition. Knowl. Based Syst. 91, 241–251 (2016)

17.

W. Deng, J. Hu, N. Zhang et al., Fine-grained face verification: FGLFW database, baselines, and human-DCMN partnership. Pattern Recognit. 66, 63–73 (2017)

18.

D. Buschek, A. De Luca, F. Alt, Improving accuracy, applicability and usability of keystroke biometrics on mobile touchscreen device, in. Proceedings of CHI, Seoul, Republic of Korea (2015), pp. 1393–1402

19.

N. Zheng, K. Bai, H. Huang et al., You are how you touch: user verification on smartphones via tapping behaviors, in Proceedings of ICNP, Raleigh, NC, USA (2014), pp. 221–232

20.

B. Draffin, J. Zhu, J. Zhang, KeySens: passive user authentication through micro-behavior modeling of soft keyboard interaction, in Proceedings of MobiCASE, Paris, France (2013), pp. 184–201

21.

T.T. Ngo, Y. Makihara, H. Nagahara et al., The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication. Pattern Recognit. 47(1):228–237 (2014)

22.

I. Youn, S. Choi, R. L. May et al., New gait metrics for biometric authentication using a 3-axis acceleration, in Proceedings of CCNC, Las Vegas, NV, USA (2014), pp. 596–601

23.

M. Muaaz, R. Mayrhofer, Orientation independent cell phone based gait authentication, in Proceedings of MOMM, Kaohsiung, Taiwan (2014), pp. 161–164

24.

M. Frank, R. Biedert, E. Ma et al., Touchalytics: on the applicability of touchscreen input as a behavioral biometric for continuous authentication. IEEE Trans. Inf. Forensics Secur. 8(1), 136–148 (2013)CrossRef

25.

C. Shen, Y. Zhang, Z. Cai et al., Touch-interaction behavior for continuous user authentication on smartphones, in Proceedings of ICB, Phuket, Thailand (2015), pp. 157–162

26.

A. De Luca, A. Hang, F. Brudy et al., Touch me once and i know it’s you!: implicit authentication based on touch screen patterns, in Proceedings of CHI, Austin, Texas, USA (2012), pp. 987–996

27.

Z. Ding, Y. Wu, A mobile authentication method based on touch screen behavior for password pattern. J. Comput. Inf. Syst. 2015(1):1–8 (2015)

28.

Z. Syed, J. Helmick, S. Banerjee et al., Effect of user posture and device size on the performance of touch-based authentication systems, in Proceedings of HASE, Daytona Beach Shores, FL, USA (2015), pp. 10–17

29.

N. Palaskar, Z. Syed, S. Banerjee et al., Empirical techniques to detect and mitigate the effects of irrevocably evolving user profiles in touch-based authentication systems, in Proceedings of HASE, Orlando, FL, USA (2016), pp. 9–16

30.

Q. Liu, M. Wang, P. Zhao et al., A behavioral authentication method for mobile gesture against resilient user posture, in Proceedings of IEEE International Conference on Systems and Informatics, Shanghai, China (2016), pp. 324–331

31.

W. Yu, C. Yan, Z. Ding et al., Modeling and verification of online shopping business processes by considering malicious behavior patterns. IEEE Trans. Autom. Sci. Eng. 13(2), 647–662 (2016)CrossRef

32.

J. Changjun, Y. Chungang, C. Hongzhong et al., Pattern construction and analysis system and its identification method for touch-screen user click behavior. 201510713975.3, 28 October 2015

33.

J. Changjun, Y. Chungang, D. Zhijun et al., Browsing behavior authentication method and system for handheld devices by integrating multiple factors. 201711033546.7, 30 October 2017

34.

J. Changjun, Y. Chungang, D. Zhijun et al., A construction method and system based on changes of postures for gesture behavior authentication, 201611106000.5, 5 December 2016

35.

L. Qiang, Research of touch screen behavior authentication method based on user posture. Tongji University Master thesis (2017)

36.

R. Meier, Professional Android 4 Application Development. Wiley, Hoboken (2012)

37.

G.E.P. Box, G.M. Jenkins, Time Series Analysis: Forecasting and Control (Wiley, Hoboken, 2015)MATH

38.

P. Senin, Dynamic time warping algorithm review. Report, Information and Computer Science Department University of Hawaii at Manoa Honolulu (2008)

39.

P.-N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining. Pearson Addison-Wesley, Hoboken (2006)

40.

I.T. Jolliffe, Principal Component Analysis (Wiley, Hoboken, 2002)MATH

41.

J.B. Macqueen, Some methods for classification and analysis of multivariate observations, in Proceedings of BSMSP, Berkeley, USA (1967), pp. 281–297

42.

P.J. Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20(1), 53–65 (1987)

43.

L. Hubert, P. Arabie, Comparing partitions. J. Classif. 2(1):193–218 (1985)

44.

B. Schölkopf, R.C. Williamson, A.J. Smola et al., Support vector method for novelty detection, in Proceedings of NIPS, Denver, Colorado, USA (1999), pp. 582–588

45.

F. Tao, Z. Liu, K.A. Kwon et al., Continuous mobile authentication using touchscreen gestures, in Proceedings of HST, Waltham, MA, USA (2013), pp. 451–456

46.

C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)MATH

47.

D.R. Cox, The regression analysis of binary sequences. J. R. Stat. Soc. Ser. B-Methodol. 20(1), 215–232 (1958)MathSciNetMATH

48.

T.M. Cover, P.E. Hart, Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)

49.

L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001)

Titel: Mobile Payment Authentication
verfasst von: Changjun Jiang
Zhong Li
Verlag: Springer Singapore
Buch: Mobile Information Service for Networks
Print ISBN: 978-981-15-4568-9

Electronic ISBN: 978-981-15-4569-6

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-981-15-4569-6_8

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