Detecção de Ataques DDoS em Redes SDN Utilizando Aprendizado de Máquina: Uma Abordagem em Microsserviços

• Victor Dias UFPA
• Murilo Silva UFPA
• Matheus Gomes UFPA
• Lucas B. Oliveira UFPA
• Diego Abreu UFPA
• João Ferreira UFPA
• Antônio Abelém UFPA

DOI: https://doi.org/10.5753/sbseg_estendido.2023.234287

Abstract

Even today, security remains a critical challenge in Software Defined Networks (SDN), including threats such as Distributed Denial of Service (DDoS) attacks. In this scenario, the use of machine learning holds promise for detecting and mitigating such attacks, where not only the model’s performance must be considered, but also its impact on the network controller’s performance. This study proposes a microservices-based approach, evaluating five machine learning models for detection. The results identified Random Forest as the most effective with an F1-Score of 98.65%. Furthermore, the microservices approach enabled the use of more complex models without compromising the performance of the SDN controller.

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