HPC for SDN Intrusion Detection: Lessons from Literature and Open Challenges
Resumo
Sistemas de Detecção de Intrusão (IDS) baseados em aprendizado profundo para Redes Definidas por Software (SDN) atingem alta precisão mas frequentemente negligenciam eficiência computacional. Este trabalho analisa nove trabalhos recentes (2020–2025) sobre IDS para SDN, examinando a lacuna entre pesquisa focada em acurácia e implantação prática de HPC. Identificamos três padrões: (1) domínio de aprendizado profundo sem avaliação de escalabilidade, (2) aceleração GPU tratada como preocupação secundária, e (3) falta de benchmarks de desempenho em tempo real. Propomos uma taxonomia de estratégias HPC e destacamos a necessidade de estudos sobre trade-offs eficiência-acurácia.
Referências
Ben Said, R., Sabir, Z., and Askerzade, I. (2023). Cnn-bilstm: A hybrid deep learning approach for network intrusion detection system in sdn. IEEE Access.
Haugerud, H., Tran, H. N., Aitsaadi, N., and Yazidi, A. (2021). A dynamic and scalable parallel network intrusion detection system using intelligent rule ordering and network function virtualization. IEEE Access.
Hnamte, V., Najar, A. A., Nguyen, H. N., Hussain, J., and Sugali, M. N. (2024). Ddos attack detection and mitigation using deep neural network in sdn environment. IEEE Access.
Javeed, D., Gao, T., Saeed, M. S., Kumar, P., Kumar, R., and Jolfael, A. (2023). A softwarized intrusion detection system for iot-enabled smart healthcare system. IEEE Access.
Muthanna, M. S. A., Alkahel, R., Muthanna, A., Rafiq, A., and Abdullah, W. A. M. (2022). Towards sdn-enabled intelligent intrusion detection system for internet of things. IEEE Access.
Shu, J., Zhou, L., Zhang, W., Du, X., and Guizani, M. (2020). Collaborative intrusion detection for vanets: A deep learning-based distributed sdn approach. IEEE Access.
Sood, K., Nosouhi, M. R., Nguyen, D. D. N., Jiang, F., Chowdhury, M., and Doss, R. (2023). Intrusion detection scheme with dimensionality reduction in next generation networks. IEEE Access.
Yang, L., Song, Y., Gao, S., Hu, A., and Xiao, B. (2022). Griffin: Real-time network intrusion detection system via ensemble of autoencoder in sdn. IEEE Access.
Zhang, Y., Jue, C., Liu, W., and Ma, Y. (2025). Gran: A sdn intrusion detection model based on graph attention network and residual learning. IEEE Access.
Haugerud, H., Tran, H. N., Aitsaadi, N., and Yazidi, A. (2021). A dynamic and scalable parallel network intrusion detection system using intelligent rule ordering and network function virtualization. IEEE Access.
Hnamte, V., Najar, A. A., Nguyen, H. N., Hussain, J., and Sugali, M. N. (2024). Ddos attack detection and mitigation using deep neural network in sdn environment. IEEE Access.
Javeed, D., Gao, T., Saeed, M. S., Kumar, P., Kumar, R., and Jolfael, A. (2023). A softwarized intrusion detection system for iot-enabled smart healthcare system. IEEE Access.
Muthanna, M. S. A., Alkahel, R., Muthanna, A., Rafiq, A., and Abdullah, W. A. M. (2022). Towards sdn-enabled intelligent intrusion detection system for internet of things. IEEE Access.
Shu, J., Zhou, L., Zhang, W., Du, X., and Guizani, M. (2020). Collaborative intrusion detection for vanets: A deep learning-based distributed sdn approach. IEEE Access.
Sood, K., Nosouhi, M. R., Nguyen, D. D. N., Jiang, F., Chowdhury, M., and Doss, R. (2023). Intrusion detection scheme with dimensionality reduction in next generation networks. IEEE Access.
Yang, L., Song, Y., Gao, S., Hu, A., and Xiao, B. (2022). Griffin: Real-time network intrusion detection system via ensemble of autoencoder in sdn. IEEE Access.
Zhang, Y., Jue, C., Liu, W., and Ma, Y. (2025). Gran: A sdn intrusion detection model based on graph attention network and residual learning. IEEE Access.
Publicado
08/07/2026
Como Citar
LIMA, Mariana Almeida; TEIXEIRA, Dernier Bruno; RODRIGUES, Antonio Wendell de O..
HPC for SDN Intrusion Detection: Lessons from Literature and Open Challenges. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DA REGIÃO NORDESTE (ERAD-NE), 7. , 2026, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 25-28.
DOI: https://doi.org/10.5753/erad-ne.2026.26665.
