Towards Intelligent Security Mechanisms for Connected Things
Resumo
A ampla adoção de dispositivos conectados e de modelos de aprendizagem de máquina permite que atacantes realizem diversos ciberataques e ataques adversariais. Assim, os objetivos desta tese são investigar e desenvolver soluções de ponta para aprimorar a segurança de sistemas, detectando de maneira eficaz e eficiente ciberataques e defendendo-os de ataques adversariais. Os seus principais resultados representam múltiplos prêmios, a publicação de oito artigos em revistas de prestígio, três artigos em conferências, duas patentes e um registro de software. Além disso, nossa pesquisa foi premiada como um dos dois únicos ganhadores em todo o mundo do Microsoft Research Ph.D. Fellowship em 2022 na área de Segurança, Privacidade e Criptografia.Referências
Freitas de Araujo-Filho, P., Kaddoum, G., Campelo, D. R., Gondim Santos, A., Macêdo, D., and Zanchettin, C. (2021). Intrusion Detection for Cyber–Physical Systems Using Generative Adversarial Networks in Fog Environment. IEEE Internet of Things J., 8(8):6247–6256.
Li, D., Chen, D., Jin, B., Shi, L., Goh, J., and Ng, S.-K. (2019). MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In Springer Int. Conf. on Artif. Neural Netw., pages 703–716.
Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., and Frossard, P. (2017). Universal Adversarial Perturbations. In Proc. of the IEEE Conf. on Comput. Vision and Pattern Recognit. (CVPR).
Nisioti, A., Mylonas, A., Yoo, P. D., and Katos, V. (2018). From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods. IEEE Commun. Surveys & Tut., 20(4):3369–3388.
O’Shea, T. J. and West, N. (2016). Radio Machine Learning Dataset Generation with GNU Radio. Proc. of the 6th GNU Radio Conf.
O’Shea, T. J., Corgan, J., and Clancy, T. C. (2016). Convolutional radio modulation recognition networks. In Int. Conf. on Eng. Appl. of Neural Networks, pages 213–226. Springer.
Pourranjbar, A., Elleuch, I., Landry-pellerin, S., and Kaddoum, G. (2023). Defense and Offence Strategies for Tactical Wireless Networks Using Recurrent Neural Networks. IEEE Trans. on Veh. Technol., pages 1–6.
Pourranjbar, A., Kaddoum, G., and Saad, W. (2022). Recurrent Neural Network-based Anti-jamming Framework for Defense Against Multiple Jamming Policies. IEEE Internet of Things J., pages 1–1.
Sadeghi, M. and Larsson, E. G. (2019). Adversarial Attacks on Deep-Learning Based Radio Signal Classification. IEEE Wireless Commun. Lett., 8(1):213–216.
Yuan, X., He, P., Zhu, Q., and Li, X. (2019). Adversarial examples: Attacks and defenses for deep learning. IEEE Trans. on Neural Netw. and Learn. Syst., 30(9):2805–2824.
Zenati, H., Romain, M., Foo, C.-S., Lecouat, B., and Chandrasekhar, V. (2018). Adversarially Learned Anomaly Detection. In IEEE Int. Conf. on Data Mining (ICDM), pages 727–736.
Zhang, L., Lambotharan, S., Zheng, G., AsSadhan, B., and Roli, F. (2021). Countermeasures Against Adversarial Examples in Radio Signal Classification. IEEE Wireless Commun. Lett., 10(8):1830–1834.
Zhang, L., Lambotharan, S., Zheng, G., Liao, G., Demontis, A., and Roli, F. (2022). A Hybrid Training-Time and Run-Time Defense Against Adversarial Attacks in Modulation Classification. IEEE Wireless Commun. Lett., 11(6):1161–1165.
Li, D., Chen, D., Jin, B., Shi, L., Goh, J., and Ng, S.-K. (2019). MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In Springer Int. Conf. on Artif. Neural Netw., pages 703–716.
Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., and Frossard, P. (2017). Universal Adversarial Perturbations. In Proc. of the IEEE Conf. on Comput. Vision and Pattern Recognit. (CVPR).
Nisioti, A., Mylonas, A., Yoo, P. D., and Katos, V. (2018). From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods. IEEE Commun. Surveys & Tut., 20(4):3369–3388.
O’Shea, T. J. and West, N. (2016). Radio Machine Learning Dataset Generation with GNU Radio. Proc. of the 6th GNU Radio Conf.
O’Shea, T. J., Corgan, J., and Clancy, T. C. (2016). Convolutional radio modulation recognition networks. In Int. Conf. on Eng. Appl. of Neural Networks, pages 213–226. Springer.
Pourranjbar, A., Elleuch, I., Landry-pellerin, S., and Kaddoum, G. (2023). Defense and Offence Strategies for Tactical Wireless Networks Using Recurrent Neural Networks. IEEE Trans. on Veh. Technol., pages 1–6.
Pourranjbar, A., Kaddoum, G., and Saad, W. (2022). Recurrent Neural Network-based Anti-jamming Framework for Defense Against Multiple Jamming Policies. IEEE Internet of Things J., pages 1–1.
Sadeghi, M. and Larsson, E. G. (2019). Adversarial Attacks on Deep-Learning Based Radio Signal Classification. IEEE Wireless Commun. Lett., 8(1):213–216.
Yuan, X., He, P., Zhu, Q., and Li, X. (2019). Adversarial examples: Attacks and defenses for deep learning. IEEE Trans. on Neural Netw. and Learn. Syst., 30(9):2805–2824.
Zenati, H., Romain, M., Foo, C.-S., Lecouat, B., and Chandrasekhar, V. (2018). Adversarially Learned Anomaly Detection. In IEEE Int. Conf. on Data Mining (ICDM), pages 727–736.
Zhang, L., Lambotharan, S., Zheng, G., AsSadhan, B., and Roli, F. (2021). Countermeasures Against Adversarial Examples in Radio Signal Classification. IEEE Wireless Commun. Lett., 10(8):1830–1834.
Zhang, L., Lambotharan, S., Zheng, G., Liao, G., Demontis, A., and Roli, F. (2022). A Hybrid Training-Time and Run-Time Defense Against Adversarial Attacks in Modulation Classification. IEEE Wireless Commun. Lett., 11(6):1161–1165.
Publicado
21/07/2024
Como Citar
ARAUJO-FILHO, Paulo Freitas de; CAMPELO, Divanilson R.; KADDOUM, Georges.
Towards Intelligent Security Mechanisms for Connected Things. In: CONCURSO DE TESES E DISSERTAÇÕES (CTD), 37. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 11-20.
ISSN 2763-8820.
DOI: https://doi.org/10.5753/ctd.2024.1833.
