User Re-Authentication via Mouse Movements and Recurrent Neural Networks

Houssel, Paul R. B.; LEIVA, Luis A.

doi:10.5220/0012296600003648

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User Re-Authentication via Mouse Movements and Recurrent Neural Networks

Houssel, Paul R. B.; LEIVA, Luis A.

2024 • In LENZINI, Gabriele (Ed.) Proceedings of the 10th International Conference on Information Systems Security and Privacy

Peer reviewed

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https://hdl.handle.net/10993/67176

DOI
10.5220/0012296600003648

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Keywords :

Authentication; Biometrics; Mouse Movements; Neural Networks; Computer Science (miscellaneous); Information Systems

Abstract :

[en] Behavioral biometrics can determine whether a user interaction has been performed by a legitimate user or an impersonator. In this regard, user re-authentication based on mouse movements has emerged as a reliable and accessible solution, without being intrusive or requiring any explicit input from the user other than regular interactions. Previous work has reported remarkably good classification performance when predicting impersonated mouse movements, however, it has relied on manual data preprocessing or ad-hoc feature extraction methods. In this paper, we design and contrast different recurrent neural networks that take as input raw mouse movements, represented by discrete sequences of coordinate derivatives (coordinate offsets relative to time), as a mean of user re-authentication that could be used on web platforms. We show that a 2-layer BiGRU model outperforms state-of-the-art approaches while being much simpler and more efficient. Our software and models are publicly available.

Disciplines :

Computer science

Author, co-author :

Houssel, Paul R. B. ; University of Luxembourg, Luxembourg

LEIVA, Luis A. ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)

External co-authors :

yes

Language :

English

Title :

User Re-Authentication via Mouse Movements and Recurrent Neural Networks

Publication date :

2024

Event name :

Proceedings of the 10th International Conference on Information Systems Security and Privacy

Event place :

Rome, Ita

Event date :

26-02-2024 => 28-02-2024

Audience :

International

Main work title :

Proceedings of the 10th International Conference on Information Systems Security and Privacy

Editor :

LENZINI, Gabriele ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > IRiSC

Publisher :

Science and Technology Publications, Lda

ISBN/EAN :

9789897586835

Peer reviewed :

Peer reviewed

European Projects :

HE - 101071147 - SYMBIOTIK - Context-aware adaptive visualizations for critical decision making

FnR Project :

FNR15722813 - BANANA - Brainsourcing For Affective Attention Estimation, 2021 (01/02/2022-31/01/2025) - Luis Leiva

Funders :

European Union

Funding text :

This work is supported by the Horizon 2020 FET program of the European Union through the ERA-NET Cofund funding (BANANA, grant CHIST-ERA-20- BCI-001) and Horizon Europe's European Innovation Council through the Pathfinder program (SYMBIOTIK, grant 101071147).

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Bibliography

Abdulrahman, S. A. and Alhayani, B. (2021). A comprehensive survey on the biometric systems based on physiological and behavioural characteristics. Materials Today: Proceedings.
Ackerson, J. M., Dave, R., and Seliya, N. (2021). Applications of recurrent neural network for biometric authentication & anomaly detection. Information, 12(7).
Almalki, S., Assery, N., and Roy, K. (2021). An empirical evaluation of online continuous authentication and anomaly detection using mouse clickstream data analysis. Appl. Sci., 11(13).
Almalki, S., Chatterjee, P., and Roy, K. (2019). Continuous authentication using mouse clickstream data analysis. In Proc. SpaCCS.
Antal, M. and Egyed-Zsigmond, E. (2019). Intrusion detection using mouse dynamics. IET Biometrics, 8(5).
Antal, M. and Fejer, N. (2020). Mouse dynamics based user recognition using deep learning. Acta Univ. Sapientiae, Informatica, 12(1).
Antal, M., Fejer, N., and Buza, K. (2021). Sapi Mouse: Mouse dynamics-based user authentication using deep feature learning. In Proc. SACI.
Bruckner, L., Arapakis, I., and Leiva, L. A. (2020). Query abandonment prediction with recurrent neural models of mouse cursor movements. In Proc. CIKM.
Cai, J., Luo, J., Wang, S., and Yang, S. (2018). Feature selection in machine learning: A new perspective. Neurocomputing, 300.
Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. CoRR, abs/1409.1259.
Chong, P., Elovici, Y., and Binder, A. (2019). User authentication based on mouse dynamics using deep neural networks: A comprehensive study. IEEE Trans. Inf. Forensics Secur., 15.
Chong, P., Tan, Y. X. M., Guarnizo, J., Elovici, Y., and Binder, A. (2018). Mouse authentication without the temporal aspect – what does a 2D-CNN learn? In Proc. SPW.
Dwarampudi, M. and Reddy, N. V. S. (2019). Effects of padding on LSTMs and CNNs. CoRR, abs/1903.07288.
Eberz, S., Rasmussen, K. B., Lenders, V., and Martinovic, I. (2017). Evaluating behavioral biometrics for continuous authentication: Challenges and metrics. In Proc. ASIACCS.
Fridman, L., Stolerman, A., Acharya, S., Brennan, P., Juola, P., Greenstadt, R., and Kamd, M. (2015). Multi-modal decision fusion for continuous authentication. Comput. Electr. Eng., 41.
Fu, S., Qin, D., Qiao, D., and Amariucai, G. T. (2020). RUMBA-mouse: Rapid user mouse-behavior authentication using a CNN-RNN approach. In Proc. CNS.
Fulop, A., Kovacs, L., Kurics, T., and Windhager-Pokol, E. (2016). Balabit mouse dynamics challenge data set. https://github.com/balabit/.
Handa, J., Singh, S., and Saraswat, S. (2019). A comparative study of mouse and keystroke based authentication. In Proc. Confluence.
Hema, D. and Bhanumathi, S. (2016). Mouse behavior based multi-factor authentication using neural networks. In Proc. ICCPCT.
Hochreiter, S. and Schmidhuber, J. (1997). Long Short Term Memory. Neural Comput., 9(8).
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA, 79(8).
Hu, T., Niu, W., Zhang, X., Liu, X., Lu, J., and Liu, Y. (2019). An insider threat detection approach based on mouse dynamics and deep learning. Secur. Commun. Netw., 2019.
Jorgensen, Z. and Yu, T. (2011). On mouse dynamics as a behavioral biometric for authentication. In Proc. ASIACCS.
Lee, H., Lee, Y., Lee, K., and Yim, K. (2016). Security assessment on the mouse data using mouse loggers. In Proc. BWCCA.
Lee, K., Esposito, C., and Lee, S.-Y. (2019). Vulnerability analysis challenges of the mouse data based on machine learning for image-based user authentication. IEEE Access, 7.
Leiva, L. A., Arapakis, I., and Iordanou, C. (2021). My mouse, my rules: Privacy issues of behavioral user profiling via mouse tracking. In Proc. CHIIR.
Leiva, L. A., Diaz, M., Ferrer, M. A., and Plamondon, R. (2020). Human or machine? it is not what you write, but how you write it. In Proc. ICPR.
Levi, M. and Hazan, I. (2020). Deep learning based sequential mining for user authentication in web applications. In Proc. ETAA.
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., and Liu, H. (2017). Feature selection: A data perspective. ACM Comput. Surv., 50(6).
Lin, C.-C., Chang, C.-C., and Liang, D. (2012). A new nonintrusive authentication approach for data protection based on mouse dynamics. In Proc. ISBAST.
Liu, Y., Jiang, Y., and Devenere, J. (2020). Using deep learning for fusion of eye and mouse movement based user authentication. In Proc. IJCB.
Muda, R., Hamid, N. A., Satar, S. D. M., Mohamad, M., Mahadi, N. A., and Ghazali, F. (2017). Mouse movement behavioral biometric for static user authentication. Advanced Science Letters, 23(6).
Oak, R. (2018). A literature survey on authentication using behavioural biometric techniques. In Proc. ICICC.
Pramila, R. M., Misbahuddin, M., and Shukla, S. (2022). A survey on adaptive authentication using machine learning techniques. In Proc. IDSCS.
Qin, D., Fu, S., Amariucai, G., Qiao, D., and Guan, Y. (2020). MAUSPAD: Mouse-based authentication using segmentation-based, progress-adjusted DTW. In Proc. TrustCom.
Revett, K., Jahankhani, H., de Magalhaes, S. T., and Santos, H. M. D. (2008). A survey of user authentication based on mouse dynamics. In Proc. ICGeS.
Rose, J., Liu, Y., and Awad, A. (2017). Biometric authentication using mouse and eye movement data. In Proc. SPW.
Roth, J., Liu, X., and Metaxas, D. (2014). On continuous user authentication via typing behavior. IEEE Trans. Image Process., 23(10).
Shen, C., Cai, Z., Guan, X., and Wang, J. (2012). On the effectiveness and applicability of mouse dynamics biometric for static authentication: A benchmark study. In Proc. ICB.
Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404.
Tan, Y. X. M., Binder, A., and Roy, A. (2017). Insights from curve fitting models in mouse dynamics authentication systems. In Proc. AINS.
Tan, Y. X. M., Iacovazzi, A., Homoliak, I., Elovici, Y., and Binder, A. (2019). Adversarial attacks on remote user authentication using behavioural mouse dynamics. In Proc. IJCNN.
Thomas, P. A. and Mathew, K. P. (2022). An efficient optimized mouse and keystroke dynamics framework for continuous non-intrusive user authentication. Wirel. Pers. Commun., 124.
Traore, I., Woungang, I., Obaidat, M. S., Nakkabi, Y., and Lai, I. (2012). Combining mouse and keystroke dynamics biometrics for risk-based authentication in web environments. In Proc. ICDH.
Wei, A., Zhao, Y., and Cai, Z. (2019). A deep learning approach to web bot detection using mouse behavioral biometrics. In Proc. CCBR.
Yamauchi, T. and Bowman, C. (2014). Mining cursor motions to find the gender, experience, and feelings of computer users. In Proc. ICDMW.
Yamauchi, T., Seo, J. H., Jett, N., Parks, G., and Bowman, C. (2015). Gender differences in mouse and cursor movements. Int. J. Hum. Comput. Interact., 31(12).
Yazji, S., Chen, X., Dick, R. P., and Scheuermann, P. (2009). Implicit user re-authentication for mobile devices. In Proc. UIC.
Zheng, Y., Yu, L., Haque, S., Zhang, P., and Elmaghraby, A. S. (2022). Improving cybersecurity through deep learning on keystroke and mouse dynamics. In Proc. SPIE Defense + Commercial Sensing