Autenticação Contínua e Segura Baseada em Sinais PPG e Comunicação Galvânica
Resumo
A autenticação biométrica suporta diferentes aplicações e tem ganho um papel fundamental nas redes vestíveis por suplantar limitações das interfaces homem-máquina de seus dispositivos. Em geral, os métodos de autenticação dependem de eventos únicos, como reconhecimento de íris ou face, exigindo a validação da identidade do usuário sempre que ele precisa acessar o sistema. Todavia, com a recente inclusão de biosensores em dispositivos vestíveis, alguns sinais são coletados constantemente, permitindo uma autenticação contínua. Entretanto, os métodos existentes de autenticação contínua via ECG e EMG são custosos, complexos ou inconvenientes para o usuário. Assim, para superar esses problemas, este artigo propõe o sistema BEAT, o qual utiliza biosinais do fotopletismograma (do inglês, photoplethysmogram-PPG) para estimar mudanças volumétricas no fluxo sanguíneo. Além disso, o sistema BEAT transmite os sinais coletados pela pele (i.e., comunicação por acoplamento galvânico), sendo o tecido epitelial um canal secundário seguro contra ataques baseados em radiofrequência. Os resultados de avaliações experimentais demonstram a viabilidade e eficiência do sistema.
Palavras-chave:
Autenticação, biosensores, Dispositivos Vestiveis
Referências
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Yamaba, H., Kurogi, A., Kubota, S.-I., Katayama, T., Park, M., and Okazaki, N. (2017). Evaluation of feature values of surface electromyograms for user authentication on mobile devices. Artificial Life and Robotics, 22(1):108–112.
Belgacem, N., Fournier, R., Nait-Ali, A., and Bereksi-Reguig, F. (2015). A novel biometric authentication approach using ECG and EMG signals. J. of Medical Engr. & Tech., 39(4):226– 238.
Blasco, J., Chen, T. M., Tapiador, J., and Peris-Lopez, P. (2016). A survey of wearable biometric recognition systems. ACM Computing Surveys (CSUR), 49(3):1–35.
Bonissi, A., Labati, R. D., Perico, L., Sassi, R., Scotti, F., and Sparagino, L. (2013). A preliminary study on continuous authentication methods for photoplethysmographic biometrics. In IEEE BIOMS, pages 28–33. IEEE.
Chen, L.-F., Liao, H.-Y. M., Ko, M.-T., Lin, J.-C., and Yu, G.-J. (2000). A new lda-based face recognition system which can solve the small sample size problem. Pattern recognition, 33(10):1713–1726.
Gao, W., Emaminejad, S., Nyein, H. Y. Y., Challa, S., Chen, K., Peck, A., Fahad, H. M., Ota, H., Shiraki, H., Kiriya, D., et al. (2016). Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature, 529(7587):509–527.
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23):215–220.
Gu, Y. and Zhang, Y. (2003). Photoplethysmographic authentication through fuzzy logic. In IEEE EMBS, pages 136–137. IEEE.
Gu, Y., Zhang, Y., and Zhang, Y. (2003). A novel biometric approach in human verification by photoplethysmographic signals. In IEEE EMBS, pages 13–14. IEEE.
Huang, Y.-P., Luo, S.-W., and Chen, E.-Y. (2002). An efficient iris recognition system. In In Proceedings of International Conference on Machine Learning and Cybernetics, volume 1, pages 450–454. IEEE.
IDTechEX (2018). Wearable technology market by 2028. https://goo.gl/7E1pKb. Accessed: 2018-12-05.
Jain, A. K., Hong, L., Pankanti, S., and Bolle, R. (1997). An identity-authentication system using fingerprints. Proceedings of the IEEE, 85(9):1365–1388.
Kim, D.-S. and Hong, K.-S. (2008). Multimodal biometric authentication using teeth image and voice in mobile environment. IEEE Transactions on Consumer Electronics, 54(4):1790–1797.
Li, Z., Han, W., and Xu, W. (2014). A large-scale empirical analysis of chinese web passwords. In USENIX Security Symposium, pages 559–574.
Liang, Y., Elgendi, M., Chen, Z., and Ward, R. (2018). An optimal filter for short photoplethysmogram signals. Scientific data, 5:180076.
Lin, F., Song, C., Zhuang, Y., Xu, W., Li, C., and Ren, K. (2017). Cardiac scan: A non-contact and continuous heart-based user authentication system. In Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking, pages 315–328. ACM.
Lourenço, A., Silva, H., and Fred, A. (2011). Unveiling the biometric potential of finger-based ecg signals. Computational intelligence and neuroscience, 2011:1–8.
Marcel, S. and Millán, J. d. R. (2007). Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 29(4):743–752.
Mazurek, M. L., Komanduri, S., Vidas, T., Bauer, L., Christin, N., Cranor, L. F., Kelley, P. G., Shay, R., and Ur, B. (2013). Measuring password guessability for an entire university. In Proceedings of the ACM SIGSAC, pages 173–186. ACM.
Miyamoto, C., Baba, S., and Nakanishi, I. (2009). Biometric person authentication using new spectral features of electroencephalogram (EEG). In IEEE ISPACS, pages 1–4. IEEE.
Mosenia, A., Sur-Kolay, S., Raghunathan, A., and Jha, N. K. (2017). Caba: Continuous authentication based on bioaura. IEEE Transactions on Computers, 66(5):759–772.
Sandhu, R. S. and Samarati, P. (1994). Access control: principle and practice. IEEE Communications Magazine, 32(9):40–48.
Singh, Y. N. and Singh, S. K. (2012). Evaluation of electrocardiogram for biometric authentication. J. Information Security, 3(1):39–48.
Spachos, P., Gao, J., and Hatzinakos, D. (2011). Feasibility study of photoplethysmographic signals for biometric identification. In IEEE DSP, pages 1–5. IEEE.
Tomlinson, W., Banou, S., Yu, C., Nogueira, M., and Chowdhury, K. (2019). Secure on-skin biometric signal transmission using galvanic coupling (to appear). In IEEE INFOCOM. IEEE.
Vasisht, D., Zhang, G., Abari, O., Lu, H.-M., Flanz, J., and Katabi, D. (2018). In-body backscatter communication and localization. In ACM SIGCOMM.
Wu, G., Wang, J., Zhang, Y., and Jiang, S. (2018). A continuous identity authentication scheme based on physiological and behavioral characteristics. Sensors, 18(1):179.
Yamaba, H., Kurogi, A., Kubota, S.-I., Katayama, T., Park, M., and Okazaki, N. (2017). Evaluation of feature values of surface electromyograms for user authentication on mobile devices. Artificial Life and Robotics, 22(1):108–112.
Publicado
06/05/2019
Como Citar
NAKAYAMA, Fernando et al.
Autenticação Contínua e Segura Baseada em Sinais PPG e Comunicação Galvânica. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 37. , 2019, Gramado.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 707-720.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2019.7397.
