Deep Learning Application in Continuous Authentication
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-61221-3_31
Reference76 articles.
1. Liao, R.-F., et al.: Deep-learning-based physical layer authentication for industrial wireless sensor networks. Sensors 19, 2440 (2019). https://doi.org/10.3390/s19112440
2. Hammad, M., Pławiak, P., Wang, K., Acharya, U.R.: ResNet‐attention model for human authentication using ECG signals. Expert Syst. 38 (2021). https://doi.org/10.1111/exsy.12547
3. Shams, T.B., Hossain, M.S., Mahmud, M.F., Tehjib, M.S., Hossain, Z., Pramanik, M.I.: EEG-based biometric authentication using machine learning: a comprehensive survey (2022). https://doi.org/10.13140/RG.2.2.33744.58886
4. Li, Y., Tao, P., Deng, S., Zhou, G.: DeFFusion: CNN-based continuous authentication using deep feature fusion. ACM Trans. Sens. Netw. 18, 1–20 (2022). https://doi.org/10.1145/3485060
5. Ackerson, J., Dave, R., Seliya, N.: Applications of recurrent neural network for biometric authentication & anomaly detection. Information (Basel) 12, 272 (2021). https://doi.org/10.3390/info12070272
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