Abstract
We propose a continuous and non-intrusive passive authentication method for the Industrial Internet of Things (IIoT) systems, based on user consecutive touch screen actions during routine work processes. In particular, utilizing both the temporal-variation characteristics of the user sequential touch screen actions and the constructed features from cumulative touch screen action trajectory images (CTTIs), we propose a novel touch-interaction based passive authentication method for IIoT systems. We use the Hidden Markov Model to characterize temporal-variation characteristics of user sequential touch screen actions, and also employ the PatternNet to depict the features of cumulative touch screen action trajectory images. Extensive experiments are conducted to illustrate the authentication performance in terms of equal-error rate and accuracy for resisting against impersonation attacks.
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References
Gilchrist, A.: Industry 4.0: the Industrial Internet of Things. Apress, Berkeley, CA (2016). https://doi.org/10.1007/978-1-4842-2047-4
Qiu, T., Chi, J., Zhou, X., Ning, Z., Atiquzzaman, M., Wu, D.O.: Edge computing in Industrial Internet of Things: architecture, advances and challenges. IEEE Commun. Surveys Tutorials 22(4), 2462–2488 (2020)
Mumtaz, S., Bo, A., Al-Dulaimi, A., Tsang, K.: Guest editorial 5G and beyond mobile technologies and applications for Industrial IoT (IIoT). IEEE Trans. Industr. Inf. 14(6), 2588–2591 (2018)
Shahzad, M., Singh, M.P.: Continuous authentication and authorization for the Internet of Things. IEEE Internet Comput. 21(2), 86–90 (2017)
Abuhamad, M., Abuhmed, T., Mohaisen, D., Nyang, D.: AUToSen: deep-learning-based implicit continuous authentication using smartphone sensors. IEEE Internet Things J. 7(6), 5008–5020 (2020)
Qin, Z., et al.: Learning-aided user identification using smartphone sensors for smart homes. IEEE Internet Things J. 6(5), 7760–7772 (2019)
Zhao, G., Zhang, P., Shen, Y., Jiang, X.: Passive user authentication utilizing behavioral biometrics for IIoT systems. IEEE Internet Things J. 9(14), 12783–12798 (2022). https://ieeexplore.ieee.org/document/9663152
Shen, C., Li, Y., Chen, Y., Guan, X., Maxion, R.A.: Performance analysis of multi-motion sensor behavior for active smartphone authentication. IEEE Trans. Inf. Forensics Secur. 13(1), 48–62 (2018)
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)
Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. J. Royal Stat. Soc. Ser. c (Appl. Stat.) 28(1), 100–108 (1979)
Baggenstoss, P.: A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces. IEEE Trans. Speech Audio Process. 9(4), 411–416 (2001)
Rabiner, L., Juang, B.: An introduction to hidden Markov models. IEEE ASSP Mag. 3(1), 4–16 (1986)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32
He, R., Hu, B.-G., Zheng, W.-S., Kong, X.-W.: Robust principal component analysis based on maximum correntropy criterion. IEEE Trans. Image Process. 20(6), 1485–1494 (2011)
Li, H., Ellis, J.G., Zhang, L., Chang, S.-F.: PatternNet: visual pattern mining with deep neural network. In: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, pp. 291–299 (2018)
Acknowledgements
This work was supported in part by the Academic Funding Project for Top Talents of Disciplines (Majors) in Universities of Anhui Province of China (No. gxbjZD2021080), the Key Project of Science Research in Universities of Anhui Province of China (No. KJ2021A1066, KJ2021A1067), the National Key R &D Program of China (No. 2018YFE0207600, 2018YFB2100403), the National Natural Science Foundation of China (No. U1736216, 61972308) and the Natural Science Basic Research Program of Shaanxi (No. 2019JC-17).
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Zhao, G., Zhang, P., Shen, Y., Jiang, X. (2022). Passive User Authentication Utilizing Consecutive Touch Action Features for IIoT Systems. In: Su, C., Sakurai, K., Liu, F. (eds) Science of Cyber Security. SciSec 2022. Lecture Notes in Computer Science, vol 13580. Springer, Cham. https://doi.org/10.1007/978-3-031-17551-0_18
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DOI: https://doi.org/10.1007/978-3-031-17551-0_18
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