Abstract
The increasing use of touchscreen mobile phones to access sensitive and personal data has given rise to the need of secure and usable authentication technique. This paper presents a novel approach to authentication on touchscreen mobile devices. The basic idea was to exploit user interaction data of touchscreen mobile phone to authenticate users based on the way they perform touch operations. A filed study was conducted to design typical touch-operation scenarios and gather users’ data. Behavioral features were extracted to accurately characterize users’ behavior. Diverse classification methods were employed to perform the authentication task. Experiments are included to demonstrate the effectiveness of the proposed approach, which achieves a false-acceptance rate of 4.05%, and a false-rejection rate of 3.27%. This level of accuracy shows that these are indeed identity information in touch behavior that can be used as a mobile authentication mechanism.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Saevanee, H., Bhatarakosol, P.: User Authentication using Combination of Behavioral Biometrics over the Touchpad acting like Touch screen of Mobile Device. In: Proc. Int’l Conf. Comp. Elect. Engineer, pp. 82–86 (2008)
Luca, A.D., Hang, A., Brudy, F., Lindner, C., Hussmann, H.: Touch Me Once and I Know It’s You!: Implicit Authentication based on Touchscreen Patterns. In: Proc. ACM Annu. Conf. Human Factors in Computing Systems, pp. 987–996 (2012)
Sae-Bae, N., Ahmed, K., Isbister, K., Memon, N.: Biometric-Rich Gestures: a Novel Approach to Authentication on Multi-Touch Devices. In: Proc. ACM Annu. Conf. Human Factors in Computing Systems, pp. 977–986 (2012)
Frank, M., Biederty, R., Ma, E., Martinovic, I., Song, D.: Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication. IEEE Trans. Info. Forensics and Security 8(1), 136–148 (2013)
Marzal, A., Vidal, E.: Computation of Normalized Edit Distance and Applications. IEEE Trans. Pattern Analysis and machine intelligence 15(9), 926–932 (1993)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. John Wiley and Sons, New York (2001)
Neal, R.M.: Bayesian Learning for Neural Networks. Springer, New York (1996)
Cortes, C., Vapnik, V.: Support Vector Networks. Machine Learning 20(3), 273–297 (1995)
Swets, J.A., Pickett, R.M.: Evaluation of Diagnostic Systems: Methods From Signal Detection Theory. Academic, New York (1982)
Bengio, S., Mariethoz, J.: A statistical Significance Test for Person Authentication. In: Proc. Speaker and Language Recognition Workshop, pp. 237–244 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Cai, Z., Shen, C., Wang, M., Song, Y., Wang, J. (2013). Mobile Authentication through Touch-Behavior Features. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds) Biometric Recognition. CCBR 2013. Lecture Notes in Computer Science, vol 8232. Springer, Cham. https://doi.org/10.1007/978-3-319-02961-0_48
Download citation
DOI: https://doi.org/10.1007/978-3-319-02961-0_48
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02960-3
Online ISBN: 978-3-319-02961-0
eBook Packages: Computer ScienceComputer Science (R0)