Abstract
The amount of sensitive data stored on mobile devices has increased. Current mobile device security schemas, such as pins, passwords, patterns, or even physiological biometrics are not secure enough to protect these data. There are several continuous or active authentication approaches that would provide an additional line of defense, designed as a security countermeasure. This paper introduces a Continuous Authentication system based on the swipe gestures as images. Therefore, we designed a Dual Input Model based on MobileNetV2. For training and testing the model, we used the two public Datasets, BioIdent and HMOG. As a result, our model achieved an EER of 10.45% which represents a good rate compared to the results of existing research.
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
References
Majeed, K., et al.: Behaviour based anomaly detection system for smartphones using machine learning algorithm. Ph.D. thesis, London Metropolitan University (2015)
Wang, C., Wang, Y., Chen, Y., Liu, H., Liu, J.: User authentication on mobile devices: approaches, threats and trends. Comput. Netw. 170, 107118 (2020)
Xu, Y., Heinly, J., White, A.M., Monrose, F., Frahm, J.M.: Seeing double: reconstructing obscured typed input from repeated compromising reflections. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, pp. 1063–1074 (2013)
Shukla, D., Kumar, R., Serwadda, A., Phoha, V.V.: Beware, your hands reveal your secrets! In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 904–917 (2014)
Aviv, A.J., Gibson, K., Mossop, E., Blaze, M., Smith, J.M.: Smudge attacks on smartphone touch screens. In: 4th USENIX Workshop on Offensive Technologies (WOOT 10) (2010)
Xi, K., Ahmad, T., Han, F., Hu, J.: A fingerprint based bio-cryptographic security protocol designed for client/server authentication in mobile computing environment. Secur. Commun. Netw. 4(5), 487–499 (2011)
Li, J., Zhang, C., Cao, Q., Qi, C., Huang, J., Xie, C.: An experimental study on deep learning based on different hardware configurations. In: 2017 International Conference on Networking, Architecture, and Storage (NAS), pp. 1–6. IEEE (2017)
Bousarhane, B., Bensiali, S., Bouzidi, D.: Road signs recognition: state-of-the-art and perspectives. Int. J. Data Anal. Tech. Strat. 13(1–2), 128–150 (2021)
Kumar, R., Phoha, V.V., Serwadda, A.: Continuous authentication of smartphone users by fusing typing, swiping, and phone movement patterns. In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–8. IEEE (2016)
Sitová, Z., Šeděnka, J., Yang, Q., Peng, G., Zhou, G., Gasti, P., Balagani, K.S.: HMOG: new behavioral biometric features for continuous authentication of smartphone users. IEEE Trans. Inf. Forensics Secur. 11(5), 877–892 (2015)
Mallet, J., Pryor, L., Dave, R., Seliya, N., Vanamala, M., Sowells-Boone, E.: Hold on and swipe: a touch-movement based continuous authentication schema based on machine learning. In: 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML), pp. 442–447. IEEE (2022)
Antal, M., Bokor, Z., Szabó, L.Z.: Information revealed from scrolling interactions on mobile devices. Pattern Recogn. Lett. 56, 7–13 (2015)
Oliphant, T.E.: Python for scientific computing. Comput. Sci. Eng. 9(3), 10–20 (2007)
Caglar, H., Caglar, N., Elfaituri, K.: B-spline interpolation compared with finite difference, finite element and finite volume methods which applied to two-point boundary value problems. Appl. Math. Comput. 175(1), 72–79 (2006)
Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Hassan, E., Shams, M.Y., Hikal, N.A., Elmougy, S.: The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study. Multimedia Tools Appl., 1–43 (2022)
Volaka, H.C., Alptekin, G., Basar, O.E., Isbilen, M., Incel, O.D.: Towards continuous authentication on mobile phones using deep learning models. Procedia Comput. Sci. 155, 177–184 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Naji, Z., Bouzidi, D. (2023). Deep Learning Approach for a Dynamic Swipe Gestures Based Continuous Authentication. In: Hassanien, A.E., et al. The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023. AICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 164. Springer, Cham. https://doi.org/10.1007/978-3-031-27762-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-27762-7_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27761-0
Online ISBN: 978-3-031-27762-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)