Abstract
Pattern passwords are one of the embedded authentication method of touchscreen devices, however it has some major drawbacks which briefly are identifiability and imitability. The password of the user is noticeable when entering the pattern due to shining circles. Therefore, what we put forward in this paper is a novel biometric implementation of a hidden system to pattern password authentication for increasing password security. As opposed to general research concept which extracts touch or keystroke durations, we focused on the touching coordinates calculated the distance of the line between the constant pattern node and the touched place as well as the angle. Using these inputs, we trained the neural network by Gauss-Newton and Levenberg-Marquardt algorithms and conducted the experiments with these trained classifiers.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zheng, N., Bai, K., Huang, H., Wang, H.: You are how you touch: User verification on smartphones via tapping behaviors, Technical report, College of William and Mary (2012)
Kwapisz, J., Weiss, G., Moore, S.: Cell phone-based biometric identification. In: Proceedings IEEE International Conference on Biometrics: Theory Applications and Systems (2010)
Chang, T.Y., Tsai, C.J., Lin, J.H.: A graphical-based password keystroke dynamic authentication system for touch screen handheld mobile devices. J. Syst. Softw. 85(5), 1157–1165 (2012)
Sae-Bae, N., Ahmed, K., Isbister, K., Memon, N.: Biometric-rich gestures: a novel approach to authentication on multi-touch devices. In: CHI 2012 Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems, New York (2012)
De Luca, A., Hang, A., Brudy, F., Lindner, C., Hussmann, H.: Touch me once and i know it’s you!: implicit authentication based on touch screen patterns. In: CHI 2012 Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems, New York (2012)
Angulo, J., Wästlund, E.: Exploring touch-screen biometrics for user identification on smart phones. In: Camenisch, J., Crispo, B., Fischer-Hübner, S., Leenes, R., Russello, G. (eds.) Privacy and Identity Management for Life. IFIP AICT, vol. 375, pp. 130–143. Springer, Heidelberg (2012)
Shahzad, M., Liu, A.X., Samuel, A.: Secure unlocking of mobile touch screen devices by simple gestures: you can see it but you can not do it. In: Proceedings of the 19th Annual International Conference on Mobile Computing & Networking. ACM (2013)
Schaub, F., Deyhle, R., Weber, M.: Password entry usability and shoulder surfing susceptibility on different smartphone platforms. In: Proceedings of Mobile and Ubiquitous Multimedia, 2012
Shahzad, M., Zahid, S., Farooq, M.: A hybrid GA-PSO fuzzy system for user identification on smart phones. In: ACM, Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 1617–1624 (2009)
Maiorana, E., Campisi, P., González-Carballo, N., Neri, A.: Keystroke dynamics authentication for mobile phones. In: Proceedings of the 2011 ACM Symposium on Applied Computing, pp. 21–26. ACM (2011)
Rao, M.K., Aparna, P., Akash, G.A., Mounica, K.: A graphical password authentication system for touch screen based devices. Int. J. Appl. Eng. Res. 9(18), 4917–4924 (2014)
Alpar, O.: Intelligent biometric pattern password authentication systems for touchscreens. Expert Syst. Appl. 42(17), 6286–6294 (2015)
Alpar, O.: Keystroke recognition in user authentication using ANN based RGB histogram technique. Eng. Appl. Artif. Intell. 32, 213–217 (2014)
Trojahn, M., Arndt, F., Ortmeier, F.: Authentication with time features for keystroke dynamics on touchscreens. In: De Decker, B., Dittmann, J., Kraetzer, C., Vielhauer, C. (eds.) CMS 2013. LNCS, vol. 8099, pp. 197–199. Springer, Heidelberg (2013)
Jeanjaitrong, N., Bhattarakosol, P.: Feasibility study on authentication based keystroke dynamic over touch-screen devices. In: 2013 13th International Symposium on Communications and Information Technologies (ISCIT), pp. 238–242. IEEE (2013)
Kambourakis, G., Damopoulos, D., Papamartzivanos, D., Pavlidakis, E.: Introducing touchstroke: keystroke‐based authentication system for smartphones. Secur. Commun. Netw. (2014). doi:10.1002/sec.1061
Tasia, C.J., Chang, T.Y., Cheng, P.C., Lin, J.H.: Two novel biometric features in keystroke dynamics authentication systems for touch screen devices. Secur. Commun. Netw. 7(4), 750–758 (2014)
Frank, M., Biedert, R., Ma, E., Martinovic, I., Song, D.: Touchalytics: On the applicability of touchscreen input as a behavioral biometric for continuous authentication. IEEE Trans. Inf. Forensics Secur. 8(1), 136–148 (2013)
Sae-Bae, N., Memon, N., Isbister, K., Ahmed, K.: Multitouch gesture-based authentication. IEEE Trans. Inf. Forensics Secur. 9(4), 568–582 (2014)
Zhao, X., Feng, T., Shi, W., Kakadiaris, I.: Mobile user authentication using statistical touch dynamics images. IEEE Trans. Inf. Forensics Secur. 9(11), 1780–1789 (2014)
Rogowski, M., Saeed, K., Rybnik, M., Tabedzki, M., Adamski, M.: User authentication for mobile devices. In: Saeed, K., Chaki, R., Cortesi, A., Wierzchoń, S. (eds.) CISIM 2013. LNCS, vol. 8104, pp. 47–58. Springer, Heidelberg (2013)
Kang, P., Cho, S.: Keystroke dynamics-based user authentication using long and free text strings from various input devices. Inf. Sci. (2014). http://dx.doi.org/10.1016/j.ins.2014.08.070
Acknowledgment
This work and the contribution were supported by project “SP/2014/05 - Smart Solutions for Ubiquitous Computing Environments” from University of Hradec Kralove, Faculty of Informatics and Management.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Alpar, O., Krejcar, O. (2015). Pattern Password Authentication Based on Touching Location. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_46
Download citation
DOI: https://doi.org/10.1007/978-3-319-24834-9_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24833-2
Online ISBN: 978-3-319-24834-9
eBook Packages: Computer ScienceComputer Science (R0)