Identity Verification in Virtual Education Using Biometric Analysis Based on Keystroke Dynamics
Abstract
Virtual education has become one of the tools most widely used by students at all educational levels, not just because of its convenience and flexibility, but also because it can expand educational coverage. All these benefits also bring along multiple issues in terms of security and reliability in the evaluation the of student’s knowledge because traditional identity verification strategies, such as the combination of username and password, do not guarantee that the student enrolled in the course really takes the exam. Therefore, a system with a different type of verification strategy should be designed to differentiate valid users from impostors. This study proposes a new verification system based on distances computed among Gaussian Mixture Models created with different writing task. The proposed approach is evaluated in two different modalities namely intrusive verification and non-intrusive verification. The intrusive mode provides a false positive rate of around 16 %, while the non-intrusive mode provides a false positive rate of 12 % In addition, the proposed strategy for non-intrusive verification is compared to a work previously reported in the literature and the results show that our approach reduces the equal error rate in about 24.3 %. The implemented strategy does not need additional hardware; only the computer keyboard is required to complete the user verification, which makes the system attractive, flexible, and practical for virtual education platforms.
References
B. Means, Y. Toyama, R. Murphy, M. Bakia, and K. Jones, “Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies,”, U.S Department of Education, Estados Unidos, Report ED-04- CO-0040 Task 0006, 2009. Available: https://repository.alt.ac.uk/629/1/US_DepEdu_Final_report_2009.pdf
T. Bretag, Handbook of Academic Integrity. Singapore: Springer Singapore, 2016. https://doi.org/10.1007/978-981-287-098-8
A. K. Jain, A. Ross, and S. Prabhakar, “An Introduction to Biometric Recognition,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 4–20, Jan. 2004. https://doi.org/10.1109/TCSVT.2003.818349
W. L. Bryan and N. Harter, “Studies in the physiology and psychology of the telegraphic language.,” Psychol. Rev., vol. 4, no. 1, pp. 27–53, 1897. https://doi.org/10.1037/h0073806
R. Joyce and G. Gupta, “Identity authentication based on keystroke latencies,” Commun. ACM, vol. 33, no. 2, pp. 168–176, Feb. 1990. https://doi.org/10.1145/75577.75582
K. Longi, J. Leinonen, H. Nygren, J. Salmi, A. Klami, and A. Vihavainen, “Identification of programmers from typing patterns,” in Proceedings of the 15th Koli Calling Conference on Computing Education Research - Koli Calling ’15, Koli Finland, 2015. pp. 60–67. https://doi.org/10.1145/2828959.2828960
S. Krishnamoorthy, L. Rueda, S. Saad, and H. Elmiligi, “Identification of User Behavioral Biometrics for Authentication Using Keystroke Dynamics and Machine Learning,” in Proceedings of the 2018 2nd International Conference on Biometric Engineering and Applications - ICBEA ’18, Amsterdam, 2018. pp. 50–57. https://doi.org/10.1145/3230820.3230829
J. R. Young, R. S. Davies, J. L. Jenkins, and I. Pfleger, “Keystroke Dynamics: Establishing Keyprints to Verify Users in Online Courses,” Comput. Sch., vol. 36, no. 1, pp. 48–68, Jan. 2019. https://doi.org/10.1080/07380569.2019.1565905
A. Morales, M. Falanga, J. Fierrez, C. Sansone, and J. Ortega-Garcia, “Keystroke dynamics recognition based on personal data: A comparative experimental evaluation implementing reproducible research,” in 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), Arlington, 2015. pp. 1–6. https://doi.org/10.1109/BTAS.2015.7358772
M. W. Shelly. Frankestein or, the modern Prometheus. London: Penguin, 2007. Available: https://books.google.com.co/books/about/Frankenstein_Or_The_Modern_Prometheus.html?id=4JzWAAAAMAAJ&redir_esc=y
D. Yu and L. Deng, Automatic Speech Recognition. London: Springer London, 2015. https://doi.org/10.1007/978-1-4471-5779-3
D. A. Reynolds, T. F. Quatieri, and R. B. Dunn, “Speaker Verification Using Adapted Gaussian Mixture Models,” Digit. Signal Process., vol. 10, no. 1–3, pp. 19–41, Jan. 2000. https://doi.org/10.1006/dspr.1999.0361
D. A. Reynolds and R. C. Rose, “Robust text-independent speaker identification using Gaussian mixture speaker models,” IEEE Trans. Speech Audio Process., vol. 3, no. 1, pp. 72–83, 1995. https://doi.org/10.1109/89.365379
M. Nishida and T. Kawahara, “Speaker model selection based on the Bayesian information criterion applied to unsupervised speaker indexing,” IEEE Trans. Speech Audio Process., vol. 13, no. 4, pp. 583–592, Jul. 2005. https://doi.org/10.1109/TSA.2005.848890
P. Mahalanobis, "On the generalized distance in statistic", National Institute of Science of India, vol 2, no 1, pp. 49-55, Apr. 1936. Available: http://library.isical.ac.in:8080/xmlui/bitstream/handle/123456789/6765/Vol02_1936_1_Art05-pcm.pdf?sequence=1&isAllowed=y
T. Arias-Vergara, J.C. Vásquez-Correa, J. R. Orozco-Arroyave, J. F Vargas-Bonilla and E. Nöth, “Parkinson's Disease Progression Assessment from Speech Using GMM-UBM”, Proceedings of Interspeech, pp 1933-1937, San Francisco, 2016. Available: https://www.isca-speech.org/archive/Interspeech_2016/pdfs/1122.PDF
A. Peacock, X. Ke, and M. Wilkerson, “Typing patterns: a key to user identification,” IEEE Secur. Priv. Mag., vol. 2, no. 5, pp. 40–47, Sep. 2004. https://doi.org/10.1109/MSP.2004.89
N. Garcia-Ospina, J.-R. Orozco-Arroyave, and J.-F. Vargas-Bonilla, “Speaker Verification System for Online Education Platforms,” in 2018 International Carnahan Conference on Security Technology (ICCST), Montreal, 2018. pp. 1–5. https://doi.org/10.1109/CCST.2018.8585602
X. Jiang, S. Wang, X. Xiang, and Y. Qian, “Integrating online i-vector into GMM-UBM for text-dependent speaker verification,” in 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala, 2017. pp. 1628–1632. https://doi.org/10.1109/APSIPA.2017.8282293
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