F-NIDS – Sistema de Detecção de Intrusão descentralizado com base em Aprendizado Federado
Resumo
O advento das redes de IoT introduziu novos desafios de escalabilidade e segurança devido ao grande número de conexões e maior taxa de transferência de dados nessas redes. Embora tenha havido esforços nos últimos anos para mitigar esses efeitos, ainda há perguntas a serem investigadas, como privacidade de dados e escalabilidade em cenários de IoT distribuídos. Este trabalho propõe que o F-NIDS é um detector de intrusão que usa a inteligência artificial federada e técnicas de privacidade diferencial, combinadas com a comunicação assíncrona entre entidades do sistema, visando escalabilidade e confidencialidade dos dados. O F-NIDS possui uma proposta de arquitetura para permitir o uso em ambientes de IoT em nuvem ou em fog. Os resultados mostraram que: o modelo de detecção confidencial do F-NIDS apresenta métricas satisfatórias de desempenho e, no caso de um ataque, prever e determinar satisfatoriamente a sua natureza.
Referências
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L. (2016). Deep learning with differential privacy. page 308-318.
Al-Yaseen, W. L., Othman, Z. A., and Nazri, M. Z. A. (2017). Real-time multi-agent system for an adaptive intrusion detection system. Pattern Recognition Letters, 85:56-64.
Bu, Z., Dong, J., Long, Q., and Su, W. (2020). Deep Learning With Gaussian Differential Privacy. Harvard Data Science Review, 2(3). https://hdsr.mitpress.mit.edu/pub/u24wj42y.
Cavalcante, I. C., Meneguette, R. I., Torres, R. H., Mano, L. Y., Gonçalves, V. P., Ueyama, J., Pessin, G., Amvame Nze, G. D., and Rocha Filho, G. P. (2022). Federated system for transport mode detection. Energies, 15(23):9256.
Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., and Faruki, P. (2019). Network intrusion detection for iot security based on learning techniques. IEEE Communications Surveys Tutorials, 21(3):2671-2701.
Chen, H., Hussain, S. U., Boemer, F., Stapf, E., Sadeghi, A. R., Koushanfar, F., and Cammarota, R. (2020). Developing privacy-preserving ai systems: The lessons learned. In 2020 57th ACM/IEEE Design Automation Conference (DAC), pages 1-4.
Dwork, C. (2006). Differential privacy. In Bugliesi, M., Preneel, B., Sassone, V., and Wegener, I., editors, Automata, Languages and Programming, pages 1-12, Berlin, Heidelberg. Springer Berlin Heidelberg.
Geraldo Filho, P., Villas, L. A., Gonçalves, V. P., Pessin, G., Loureiro, A. A., and Ueyama, J. (2019). Energy-efficient smart home systems: Infrastructure and decision-making process. Internet of Things, 5:153-167.
Habibzadeh, H., Nussbaum, B. H., Anjomshoa, F., Kantarci, B., and Soyata, T. (2019). A survey on cybersecurity, data privacy, and policy issues in cyber-physical system deployments in smart cities. Sustainable Cities and Society, 50:101660.
Kundu, A. (2021). Fed+: A unified approach to robust personalized federated learning.
M., R., Ahmed, M., and Khan, R. (2019). An adaptive distributed intrusion detection system architecture using multi agents. International Journal of Electrical and Computer Engineering (IJECE), 9:4951.
McMahan, B., Moore, E., Ramage, D., Hampson, S., and Arcas, B. A. y. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. In Singh, A. and Zhu, J., editors, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, volume 54 of Proceedings of Machine Learning Research, pages 1273-1282. PMLR.
Qian, B., Su, J., Wen, Z., Jha, D. N., Li, Y., Guan, Y., Puthal, D., James, P., Yang, R., Zomaya, A. Y., Rana, O., Wang, L., Koutny, M., and Ranjan, R. (2020). Orchestrating the development lifecycle of machine learning-based iot applications: A taxonomy and survey. ACM Comput. Surv., 53(4).
Rahman, M. A. and Asyhari, A. T. (2019). The emergence of internet of things (iot): Connecting anything, anywhere. Computers, 8(2).
Roman, R., Zhou, J., and Lopez, J. (2013). On the features and challenges of security and privacy in distributed internet of things. Computer Networks, 57(10):2266-2279. Towards a Science of Cyber Security Security and Identity Architecture for the Future Internet.
Ruzafa-Alcazar, P., Fernandez-Saura, P., Marmol-Campos, E., Gonzalez-Vidal, A., Hernandez Ramos, J. L., Bernal, J., and Skarmeta, A. F. (2021). Intrusion detection based on privacy-preserving federated learning for the industrial iot. IEEE Transactions on Industrial Informatics, pages 1-1.
Salem, A., Sautter, Y., Backes, M., Humbert, M., and Zhang, Y. (2020). Baaan: Backdoor attacks against autoencoder and gan-based machine learning models.
Sarhan, M., Layeghy, S., and Portmann, M. (2022). Towards a standard feature set for network intrusion detection system datasets. Mobile Networks and Applications, 27(1):357-370.
Tabassum, A., Erbad, A., Mohamed, A., and Guizani, M. (2021). Privacy-preserving distributed ids using incremental learning for iot health systems. IEEE Access, 9:14271-14283.
Zhao, R., Yin, Y., Shi, Y., and Xue, Z. (2020). Intelligent intrusion detection based on federated learning aided long short-term memory. Physical Communication, 42:101157.
Zhu, H., Zhang, H., and Jin, Y. (2021). From federated learning to federated neural architecture search: a survey. Complex & Intelligent Systems, 7.
Al-Yaseen, W. L., Othman, Z. A., and Nazri, M. Z. A. (2017). Real-time multi-agent system for an adaptive intrusion detection system. Pattern Recognition Letters, 85:56-64.
Bu, Z., Dong, J., Long, Q., and Su, W. (2020). Deep Learning With Gaussian Differential Privacy. Harvard Data Science Review, 2(3). https://hdsr.mitpress.mit.edu/pub/u24wj42y.
Cavalcante, I. C., Meneguette, R. I., Torres, R. H., Mano, L. Y., Gonçalves, V. P., Ueyama, J., Pessin, G., Amvame Nze, G. D., and Rocha Filho, G. P. (2022). Federated system for transport mode detection. Energies, 15(23):9256.
Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., and Faruki, P. (2019). Network intrusion detection for iot security based on learning techniques. IEEE Communications Surveys Tutorials, 21(3):2671-2701.
Chen, H., Hussain, S. U., Boemer, F., Stapf, E., Sadeghi, A. R., Koushanfar, F., and Cammarota, R. (2020). Developing privacy-preserving ai systems: The lessons learned. In 2020 57th ACM/IEEE Design Automation Conference (DAC), pages 1-4.
Dwork, C. (2006). Differential privacy. In Bugliesi, M., Preneel, B., Sassone, V., and Wegener, I., editors, Automata, Languages and Programming, pages 1-12, Berlin, Heidelberg. Springer Berlin Heidelberg.
Geraldo Filho, P., Villas, L. A., Gonçalves, V. P., Pessin, G., Loureiro, A. A., and Ueyama, J. (2019). Energy-efficient smart home systems: Infrastructure and decision-making process. Internet of Things, 5:153-167.
Habibzadeh, H., Nussbaum, B. H., Anjomshoa, F., Kantarci, B., and Soyata, T. (2019). A survey on cybersecurity, data privacy, and policy issues in cyber-physical system deployments in smart cities. Sustainable Cities and Society, 50:101660.
Kundu, A. (2021). Fed+: A unified approach to robust personalized federated learning.
M., R., Ahmed, M., and Khan, R. (2019). An adaptive distributed intrusion detection system architecture using multi agents. International Journal of Electrical and Computer Engineering (IJECE), 9:4951.
McMahan, B., Moore, E., Ramage, D., Hampson, S., and Arcas, B. A. y. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. In Singh, A. and Zhu, J., editors, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, volume 54 of Proceedings of Machine Learning Research, pages 1273-1282. PMLR.
Qian, B., Su, J., Wen, Z., Jha, D. N., Li, Y., Guan, Y., Puthal, D., James, P., Yang, R., Zomaya, A. Y., Rana, O., Wang, L., Koutny, M., and Ranjan, R. (2020). Orchestrating the development lifecycle of machine learning-based iot applications: A taxonomy and survey. ACM Comput. Surv., 53(4).
Rahman, M. A. and Asyhari, A. T. (2019). The emergence of internet of things (iot): Connecting anything, anywhere. Computers, 8(2).
Roman, R., Zhou, J., and Lopez, J. (2013). On the features and challenges of security and privacy in distributed internet of things. Computer Networks, 57(10):2266-2279. Towards a Science of Cyber Security Security and Identity Architecture for the Future Internet.
Ruzafa-Alcazar, P., Fernandez-Saura, P., Marmol-Campos, E., Gonzalez-Vidal, A., Hernandez Ramos, J. L., Bernal, J., and Skarmeta, A. F. (2021). Intrusion detection based on privacy-preserving federated learning for the industrial iot. IEEE Transactions on Industrial Informatics, pages 1-1.
Salem, A., Sautter, Y., Backes, M., Humbert, M., and Zhang, Y. (2020). Baaan: Backdoor attacks against autoencoder and gan-based machine learning models.
Sarhan, M., Layeghy, S., and Portmann, M. (2022). Towards a standard feature set for network intrusion detection system datasets. Mobile Networks and Applications, 27(1):357-370.
Tabassum, A., Erbad, A., Mohamed, A., and Guizani, M. (2021). Privacy-preserving distributed ids using incremental learning for iot health systems. IEEE Access, 9:14271-14283.
Zhao, R., Yin, Y., Shi, Y., and Xue, Z. (2020). Intelligent intrusion detection based on federated learning aided long short-term memory. Physical Communication, 42:101157.
Zhu, H., Zhang, H., and Jin, Y. (2021). From federated learning to federated neural architecture search: a survey. Complex & Intelligent Systems, 7.
Publicado
22/05/2023
Como Citar
OLIVEIRA, Jonathas A. de; MENEGUETTE, Rodolfo I.; GONÇALVES, Vinícius P.; SOUSA JR., Rafael T. de; GUIDONI, Daniel L.; OLIVEIRA, José C. M.; ROCHA FILHO, Geraldo P..
F-NIDS – Sistema de Detecção de Intrusão descentralizado com base em Aprendizado Federado. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 41. , 2023, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 29-42.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2023.426.
