Sistema de Detecção de Intrusões baseado em Aprendizagem por Reforço Federada

• André Santos Rocha UNICAMP
• Allan M. de Souza UNICAMP http://orcid.org/0000-0002-5518-8392

DOI: https://doi.org/10.5753/sbrc_estendido.2025.9517

Resumo

Nesse projeto, objetiva-se aplicar técnicas de Aprendizagem Federada por Reforço, (Federated Reinforcement Learning - FRL) ao contexto de detecção de intrusões em cenários de Internet das Coisas (Internet of Things - IoT). Diversos sistemas IoT são essenciais e compartilham informações sensíveis que precisam trafegar com segurança e privacidade. Sendo assim, é indispensável monitorar tais sistemas, evitando e defendendo ataques que visem expor seus conteúdos ou fragilizá-los. Nesse sentido, propõe-se um modelo de detecção de anomalias com FRL capaz de avaliar e detectar ataques com eficiência, sem, no entanto, compartilhar os dados locais nesse processo.

Palavras-chave: Segurança, IoT, Federado, Intrusões, Reforço, Distribuídos

Referências

Alavizadeh, H., Alavizadeh, H., and Jang-Jaccard, J. (2022). Deep q-learning based reinforcement learning approach for network intrusion detection. Computers, 11(3):41.

Figueiredo Prudencio, R., Maximo, M. R. O. A., and Colombini, E. L. (2024). A survey on offline reinforcement learning: Taxonomy, review, and open problems. IEEE Transactions on Neural Networks and Learning Systems, 35(8):10237–10257.

Harmon, M. E. and Harmon, S. S. (1996). Reinforcement learning: A tutorial. WL/AAFC, WPAFB Ohio, 45433:237–285.

Lopez-Martin, M., Carro, B., and Sanchez-Esguevillas, A. (2020). Application of deep reinforcement learning to intrusion detection for supervised problems. Expert Systems with Applications, 141:112963.

Mazetto, B. O. Aprendizado federado para detecção de intrusões.

Mowla, N. I., Tran, N. H., Doh, I., and Chae, K. (2020). AFRL: Adaptive federated reinforcement learning for intelligent jamming defense in FANET. Journal of Communications and Networks, 22(3):244–258.

Otoum, S., Guizani, N., and Mouftah, H. (2021). Federated reinforcement learning-supported IDS for IoT-steered healthcare systems. In ICC 2021 - IEEE International Conference on Communications, pages 1–6.

Pinto Neto, E. C., Sadeghi, S., Zhang, X., and Dadkhah, S. (2023). Federated reinforcement learning in IoT: Applications, opportunities and open challenges. Applied Sciences, 13(11).

Qi, J., Zhou, Q., Lei, L., and Zheng, K. (2021). Federated reinforcement learning: techniques, applications, and open challenges. Intelligence & Robotics.

Sun, Z., Niu, X., and Wei, E. (2024). Understanding generalization of federated learning via stability: Heterogeneity matters. In Dasgupta, S., Mandt, S., and Li, Y., editors, Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, volume 238 of Proceedings of Machine Learning Research, pages 676–684. PMLR.

Vadigi, S., Sethi, K., Mohanty, D., Das, S. P., and Bera, P. (2023). Federated reinforcement learning based intrusion detection system using dynamic attention mechanism. Journal of Information Security and Applications, 78:103608.

Wang, X., Garg, S., Lin, H., Hu, J., Kaddoum, G., Jalil Piran, M., and Hossain, M. S. (2022). Toward accurate anomaly detection in industrial internet of things using hierarchical federated learning. IEEE Internet of Things Journal, 9(10):7110–7119.

Wang, X., Wang, S., Liang, X., Zhao, D., Huang, J., Xu, X., Dai, B., and Miao, Q. (2024). Deep reinforcement learning: A survey. IEEE Transactions on Neural Networks and Learning Systems, 35(4):5064–5078.

Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., and Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216:106775.