A Federated Learning Approach for Authentication and User Identification based on Behavioral Biometrics

• Rafael Veiga UFPA
• Cristiano B. Both UNISINOS
• Iago Medeiros UFPA
• Denis Rosário UFPA
• Eduardo Cerqueira UFPA

DOI: https://doi.org/10.5753/sbrc.2023.536

Resumo

A smartphone can collect behavioral data without requiring additional actions on the user’s part and without the need for additional hardware. In an active or continuous user authentication process, information from integrated sensors, such as touch, and gyroscope, is used to monitor the user continuously. These sensors can capture behavioral (touch patterns, accelerometer) or physiological (fingerprint, face) data of the user naturally interacting with the device. However, transferring data from multiple users’ mobile devices to a server is not recommended due to user data privacy concerns. This paper introduces an Federated Learning (FL) approach to define a user’s biometric behavior pattern for continuous user identification and authentication. We also evaluate whether FL can be helpful in behavioral biometrics. Evaluation results compare CNNs in different epochs using FL and a centralized method with low chances of wrong predictions in user identification by the gyroscope.

Referências

Acien, A., Morales, A., Fierrez, J., Vera-Rodriguez, R., and Bartolome, I. (2020). Becaptcha: Detecting human behavior in smartphone interaction using multiple inbuilt sensors. arXiv preprint arXiv:2002.00918.

Barros, A., Resque, P., Almeida, J., Mota, R., Oliveira, H., Rosário, D., and Cerqueira, E. (2020). Data improvement model based on ecg biometric for user authentication and identification. Sensors, 20(10):2920.

Barros, A., Rosário, D., Cerqueira, E., and da Fonseca, N. L. (2021). A strategy to the reduction of communication overhead and overfitting in federated learning. In 26th Workshop on Management and Operation of Networks and Service (WGRS), pages 1-13. SBC.

Centeno, M. P., Guan, Y., and van Moorsel, A. (2018). Mobile based continuous authentication using deep features. In Proceedings of the 2nd International Workshop on Embedded and Mobile Deep Learning (EMDL), pages 19-24.

Dahia, G., Jesus, L., and Pamplona Segundo, M. (2020). Continuous authentication using biometrics: An advanced review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(4):e1365.

Ek, S., Portet, F., Lalanda, P., and Vega, G. (2020). Evaluation of federated learning aggregation algorithms: Application to human activity recognition. In ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiCompISWC '20), page 638-643, New York, NY, USA. ACM.

Espín López, J. M., Huertas Celdrán, A., Marín-Blázquez, J. G., Esquembre, F., and Martínez Pérez, G. (2021). S3: An ai-enabled user continuous authentication for smartphones based on sensors, statistics and speaker information. Sensors, 21.

Hammerla, N. Y., Halloran, S., and Plötz, T. (2016). Deep, convolutional, and recurrent models for human activity recognition using wearables. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI'16), pages 1533-1540.

Ismail Fawaz, H., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D. F., Weber, J., Webb, G. I., Idoumghar, L., Muller, P.-A., and Petitjean, F. (2020). Inceptiontime: Finding alexnet for time series classification. Data Mining and Knowledge Discovery, 34(6):1936-1962.

Kandar, S., Pal, S., and Dhara, B. C. (2021). A biometric based remote user authentication technique using smart card in multi-server environment. Wireless Personal Communications, 120(2):1003-1026.

Li, C., Niu, D., Jiang, B., Zuo, X., and Yang, J. (2021a). Meta-har: Federated representation learning for human activity recognition. In The Web Conference 2021 (WWW '21), Virtual Event / Ljubljana, Slovenia, April 19-23, 2021, pages 912-922. ACM.

Li, T., Zhang, M., Cao, H., Li, Y., Tarkoma, S., and Hui, P. (2020a). "what apps did you use?": Understanding the long-term evolution of mobile app usage. In Proceedings of The Web Conference 2020, page 66-76, New York, NY, USA. ACM.

Li, Y., Hu, H., Zhu, Z., and Zhou, G. (2020b). Scanet: Sensor-based continuous authentication with two-stream convolutional neural networks. ACM Transactions on Sensor Networks, 16.

Li, Y., Tao, P., Deng, S., and Zhou, G. (2021b). Deffusion: Cnn-based continuous authentication using deep feature fusion. ACM Trans. Sen. Netw., 18(2).

Liang, Y., Samtani, S., Guo, B., and Yu, Z. (2020). Behavioral biometrics for continuous authentication in the internet of things era: An artificial intelligence perspective. IEEE Internet of Things Journal, PP:1-1.

Lobato, W., Da Costa, J. B., de Souza, A. M., Rosário, D., Sommer, C., and Villas, L. A. (2022). Flexe: Investigating federated learning in connected autonomous vehicle simulations. In 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), pages 1-5. IEEE.

Mekruksavanich, S. and Jitpattanakul, A. (2021). Deep learning approaches for continuous authentication based on activity patterns using mobile sensing. Sensors, 21(22).

Nemes, S. and Antal, M. (2021). Feature learning for accelerometer based gait recognition. In 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI), pages 479-484.

Papamichail, M. D., Chatzidimitriou, K. C., Karanikiotis, T., Oikonomou, N.-C. I., Symeonidis, A. L., and Saripalle, S. K. (2019). Brainrun: A behavioral biometrics dataset towards continuous implicit authentication. Data, 4(2).

Pires, I. M., Marques, G., Garcia, N. M., Flórez-Revuelta, F., Canavarro Teixeira, M., Zdravevski, E., Spinsante, S., and Coimbra, M. (2020). Pattern recognition techniques for the identification of activities of daily living using a mobile device accelerometer. Electronics, 9(3):509.

Stylios, I., Kokolakis, S., Thanou, O., and Chatzis, S. (2021). Behavioral biometrics & continuous user authentication on mobile devices: A survey. Information Fusion, 66:76-99.

Wang, Z., Yan, W., and Oates, T. (2017). Time series classification from scratch with deep neural networks: A strong baseline. In 2017 International joint conference on neural networks (IJCNN), pages 1578-1585. IEEE.

Zhu, T., Qu, Z., Xu, H., Zhang, J., Shao, Z., Chen, Y., Prabhakar, S., and Yang, J. (2019). Riskcog: Unobtrusive real-time user authentication on mobile devices in the wild. IEEE Transactions on Mobile Computing, 19(2):466-483.