Desafios e Oportunidades na Detecção de Anomalias em SDN Usando Inteligência Computacional
Resumo
A complexidade crescente das redes de computadores, impulsionada pelas Redes Definidas por Software (SDN), exige técnicas avançadas de Inteligência Computacional (IC) para melhorar segurança e confiabilidade. Neste contexto, este trabalho realiza um mapeamento sistemático da literatura sobre o uso da IC na detecção de anomalias em SDN. A análise revela diversas técnicas e arquiteturas, além da importância de datasets variados, incluindo redes reais, para garantir a generalização dos modelos. São discutidos desafios como alto custo computacional, segurança dos módulos de IC e interpretabilidade dos modelos. Por fim, o estudo destaca oportunidades e a necessidade de arquiteturas flexíveis que integrem diferentes técnicas de IC para aprimorar a detecção de anomalias em SDN.
Referências
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Dawoud, A. A., Shahristani, S. S., and Raun, C. (2018). A deep learning framework to enhance software defined networks security. In 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE.
Dinh, P. T. and Park, M. (2021). R-edos: Robust economic denial of sustainability detection in an sdn-based cloud through stochastic recurrent neural network. IEEE Access.
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Jagadeesan, L. J. and Mendiratta, V. (2016). Programming the network: Application software faults in software-defined networks. In 2016 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), pages 125–131. IEEE.
Krzemien, W., Jedrasiak, K., Nawrat, A., and Daniec, K. (2021). Anomaly detection in software-defined networks using cross-validation. In 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), pages 1–7. IEEE.
Le, D.-H., Tran, H.-A., Souihi, S., and Mellouk, A. (2021). An ai-based traffic matrix prediction solution for software-defined network. In ICC 2021 - IEEE International Conference on Communications, pages 1–6. IEEE.
Mathas, C. M., Segou, O. E., Xylouris, G., Christinakis, D., Kourtis, M.-A., Vassilakis, C., and Kourtis, A. (2018). Evaluation of apache spot’s machine learning capabilities in an sdn/nfv enabled environment. In Proceedings of the 13th International Conference on Availability, Reliability and Security, ARES 2018.
Min, W., Almughalles, W., Muthanna, M. S. A., Ouamri, M. A., Muthanna, A., Hong, S., and Abd El-Latif, A. A. (2024). An sdn-orchestrated artificial intelligence-empowered framework to combat intrusions in the next generation cyber-physical systems. HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES, 14.
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Oliveira, I., Neto, E., Immich, R., Fontes, R., Neto, A., Rodriguez, F., and Rothenberg, C. E. (2021). dh-aes-p4: On-premise encryption and in-band key-exchange in p4 fully programmable data planes. In 2021 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), pages 148–153.
Pan, X., Yang, H., Xu, Z., and Zhu, Z. (2022). Adversarial analysis of ml-based anomaly detection in multi-layer network automation. In Journal of Lightwave Technology, volume 40, pages 4934–4944. IEEE.
Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M. (2008). Systematic mapping studies in software engineering. In Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, EASE’08.
Phan, T. V., Nguyen, T. G., Dao, N.-N., Huong, T. T., Thanh, N. H., and Bauschert, T. (2020). Deepguard: Efficient anomaly detection in sdn with fine-grained traffic flow monitoring. IEEE Transactions on Network and Service Management, 17(3).
Protogerou, A. and et. al. (2022). Time series network data enabling distributed intelligence. a holistic iot security platform solution. In Electronics. MDPI.
Qi, Q., Shen, R., Wang, J., Sun, H., Guo, S., and Liao, J. (2021). Spatial-temporal learning-based artificial intelligence for it operations in the edge network. In IEEE Network, volume 35, pages 197–203. IEEE.
Santos da Silva, A., Wickboldt, J. A., Granville, L. Z., and Schaeffer-Filho, A. (2016). Atlantic: A framework for anomaly traffic detection, classification, and mitigation in sdn. In 2016 IEEE/IFIP Network Operations and Management Symposium. IEEE.
Silva, D., Fontes, R., Neto, A., Silva, G., and Immich, R. (2023). Esquema de autenticação e acordo de chaves para internet das coisas. In Anais do XXVIII Workshop de Gerência e Operação de Redes e Serviços, pages 125–138, Porto Alegre, RS, Brasil. SBC.
Song, C., Park, Y., Golani, K., Kim, Y., Bhatt, K., and Goswami, K. (2017). Machine-learning based threat-aware system in software defined networks. In 2017 26th International Conference on Computer Communication and Networks (ICCCN). IEEE.
Starke, A., McNair, J., Trevizan, R., Bretas, A., Peeples, J., and Zare, A. (2018). Toward resilient smart grid communications using distributed sdn with ml-based anomaly detection. In Wired/Wireless Internet Communications.
Tsogbaatar, E., Bhuyan, M. H., Taenaka, Y., Fall, D., Gonchigsumlaa, K., Elmroth, E., and Kadobayashi, Y. (2020). SDN-Enabled IoT Anomaly Detection Using Ensemble Learning. IFIP Advances in Information and Communication Technology. Springer.
Zabeehullah, Arif, F., Khan, N. A., Haq, Q. M. u., Asim, M., and Ahmad, S. (2024). An sdn-ai-based approach for detecting anomalies in imbalance data within a network of smart medical devices. IEEE Consumer Electronics Magazine, 13(6):28–36.
Zhao, Y., Yan, B., Liu, D., He, Y., Wang, D., and Zhang, J. (2018). Soon: self-optimizing optical networks with machine learning. Opt. Express, 26(22):28713–28726.
Abubakar, A. and Pranggono, B. (2017). Machine learning based intrusion detection system for software defined networks. In 2017 seventh international conference on emerging security technologies (EST), pages 138–143. IEEE, IEEE.
Al-Ameer, A., Asraa, A., and Bhaya, W. S. (2023). Intelligent intrusion detection based on multi-model federated learning for software defined network. International Journal of Safety & Security Engineering, 13(6).
Alshammari, N. et al. (2024). Security monitoring and management for the network services in the orchestration of sdn-nfv environment using machine learning techniques. Computer Systems Science and Engineering, 48(2):363–394.
AĞCA, M. A., Faye, S., and Khadraoui, D. (2023). Trusted distributed artificial intelligence (tdai). IEEE Access, 11:113307–113323.
Bagaa, M., Taleb, T., Bernabe, J. B., and Skarmeta, A. (2020). A machine learning security framework for iot systems. IEEE Access, 8:114066–114077.
Bittencourt, L., Immich, R., Sakellariou, R., Fonseca, N., Madeira, E., Curado, M., Villas, L., DaSilva, L., Lee, C., and Rana, O. (2018). The internet of things, fog and cloud continuum: Integration and challenges. Internet of Things, 3-4:134 – 155.
Boero, L., Marchese, M., and Zappatore, S. (2017). Support vector machine meets software defined networking in ids domain. In Inter. Teletraffic Congress (ITC). IEEE.
Das, T., Shukla, R., and Sengupta, S. (2021). The devil is in the details: Confident & explainable anomaly detector for software-defined networks. pages 1–5.
Dawoud, A. A., Shahristani, S. S., and Raun, C. (2018). A deep learning framework to enhance software defined networks security. In 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE.
Dinh, P. T. and Park, M. (2021). R-edos: Robust economic denial of sustainability detection in an sdn-based cloud through stochastic recurrent neural network. IEEE Access.
do Prado, P. F., Peixoto, M. L. M., Araújo, M. C., Gama, E. S., Gonçalves, D. M., Silva, M. V. S., Immich, R., Madeira, E. R. M., and Bittencourt, L. F. (2021). Mobile Edge Computing for Content Distribution and Mobility Support in Smart Cities, pages 473–500. Springer International Publishing, Cham.
Dybå, T. and Dingsøyr, T. (2008). Empirical studies of agile software development: A systematic review. Information and Software Technology, 50(9-10):833–859.
Jagadeesan, L. J. and Mendiratta, V. (2016). Programming the network: Application software faults in software-defined networks. In 2016 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), pages 125–131. IEEE.
Krzemien, W., Jedrasiak, K., Nawrat, A., and Daniec, K. (2021). Anomaly detection in software-defined networks using cross-validation. In 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), pages 1–7. IEEE.
Le, D.-H., Tran, H.-A., Souihi, S., and Mellouk, A. (2021). An ai-based traffic matrix prediction solution for software-defined network. In ICC 2021 - IEEE International Conference on Communications, pages 1–6. IEEE.
Mathas, C. M., Segou, O. E., Xylouris, G., Christinakis, D., Kourtis, M.-A., Vassilakis, C., and Kourtis, A. (2018). Evaluation of apache spot’s machine learning capabilities in an sdn/nfv enabled environment. In Proceedings of the 13th International Conference on Availability, Reliability and Security, ARES 2018.
Min, W., Almughalles, W., Muthanna, M. S. A., Ouamri, M. A., Muthanna, A., Hong, S., and Abd El-Latif, A. A. (2024). An sdn-orchestrated artificial intelligence-empowered framework to combat intrusions in the next generation cyber-physical systems. HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES, 14.
Nobakht, M., Sivaraman, V., and Boreli, R. (2016). A host-based intrusion detection and mitigation framework for smart home iot using openflow. In 2016 11th International Conference on Availability, Reliability and Security (ARES), pages 147–156. IEEE.
Oliveira, I., Neto, E., Immich, R., Fontes, R., Neto, A., Rodriguez, F., and Rothenberg, C. E. (2021). dh-aes-p4: On-premise encryption and in-band key-exchange in p4 fully programmable data planes. In 2021 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), pages 148–153.
Pan, X., Yang, H., Xu, Z., and Zhu, Z. (2022). Adversarial analysis of ml-based anomaly detection in multi-layer network automation. In Journal of Lightwave Technology, volume 40, pages 4934–4944. IEEE.
Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M. (2008). Systematic mapping studies in software engineering. In Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, EASE’08.
Phan, T. V., Nguyen, T. G., Dao, N.-N., Huong, T. T., Thanh, N. H., and Bauschert, T. (2020). Deepguard: Efficient anomaly detection in sdn with fine-grained traffic flow monitoring. IEEE Transactions on Network and Service Management, 17(3).
Protogerou, A. and et. al. (2022). Time series network data enabling distributed intelligence. a holistic iot security platform solution. In Electronics. MDPI.
Qi, Q., Shen, R., Wang, J., Sun, H., Guo, S., and Liao, J. (2021). Spatial-temporal learning-based artificial intelligence for it operations in the edge network. In IEEE Network, volume 35, pages 197–203. IEEE.
Santos da Silva, A., Wickboldt, J. A., Granville, L. Z., and Schaeffer-Filho, A. (2016). Atlantic: A framework for anomaly traffic detection, classification, and mitigation in sdn. In 2016 IEEE/IFIP Network Operations and Management Symposium. IEEE.
Silva, D., Fontes, R., Neto, A., Silva, G., and Immich, R. (2023). Esquema de autenticação e acordo de chaves para internet das coisas. In Anais do XXVIII Workshop de Gerência e Operação de Redes e Serviços, pages 125–138, Porto Alegre, RS, Brasil. SBC.
Song, C., Park, Y., Golani, K., Kim, Y., Bhatt, K., and Goswami, K. (2017). Machine-learning based threat-aware system in software defined networks. In 2017 26th International Conference on Computer Communication and Networks (ICCCN). IEEE.
Starke, A., McNair, J., Trevizan, R., Bretas, A., Peeples, J., and Zare, A. (2018). Toward resilient smart grid communications using distributed sdn with ml-based anomaly detection. In Wired/Wireless Internet Communications.
Tsogbaatar, E., Bhuyan, M. H., Taenaka, Y., Fall, D., Gonchigsumlaa, K., Elmroth, E., and Kadobayashi, Y. (2020). SDN-Enabled IoT Anomaly Detection Using Ensemble Learning. IFIP Advances in Information and Communication Technology. Springer.
Zabeehullah, Arif, F., Khan, N. A., Haq, Q. M. u., Asim, M., and Ahmad, S. (2024). An sdn-ai-based approach for detecting anomalies in imbalance data within a network of smart medical devices. IEEE Consumer Electronics Magazine, 13(6):28–36.
Zhao, Y., Yan, B., Liu, D., He, Y., Wang, D., and Zhang, J. (2018). Soon: self-optimizing optical networks with machine learning. Opt. Express, 26(22):28713–28726.
Publicado
20/07/2025
Como Citar
FERNANDES, Rivaldo; KREUTZ, Diego; FONTES, Ramon; GOMES, Rafael Lopes; IMMICH, Roger.
Desafios e Oportunidades na Detecção de Anomalias em SDN Usando Inteligência Computacional. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 17. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 101-110.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2025.9106.
