An Efficient Optimized Mouse and Keystroke Dynamics Framework for Continuous Non-Intrusive User Authentication
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-021-09363-6.pdf
Reference28 articles.
1. Andrean, A., Jayabalan, M., & Thiruchelvam, V. (2020). Keystroke Dynamics Based User Authentication using Deep Multilayer Perceptron. International Journal of Machine Learning and Computing, 10(1), 134–139. https://doi.org/10.18178/ijmlc.2020.10.1.910
2. Antal, M., & Egyed-Zsigmond, E. (2019). Intrusion detection using mouse dynamics. IET Biometrics, 8(5), 285–294. https://doi.org/10.1049/iet-bmt.2018.5126
3. Bernardi, M. L., Cimitile, M., Martinelli, F., & Mercaldo, F. (2019). Keystroke analysis for user identification using deep neural networks. In 2019 international joint conference on neural networks (IJCNN). IEEE. https://doi.org/10.1109/IJCNN.2019.8852068
4. Biswas, D., Everson, L., Liu, M., Panwar, M., Verhoef, B. E., Patki, S., Kim, C. H., Acharyya, A., Hoof, C. V., Konijnenburg, M., & Helleputte, N. V. (2019). CorNET: Deep learning framework for PPG-based heart rate estimation and biometric identification in ambulant environment. IEEE Transactions on Biomedical Circuits and Systems, 13(2), 282–291. https://doi.org/10.1109/TBCAS.2019.2892297
5. Brocardo, M. L., Traore, I., & Woungang, I. (2019). Continuous authentication using writing style. In Biometric-based physical and cybersecurity systems (pp. 211–232). Springer. https://doi.org/10.1007/978-3-319-98734-7_8
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