Detecção de ataques DDoS usando correlação espaço-temporal bayesiana

  • Gabriel Mendonça UFRJ
  • Gustavo H. A. Santos UFRJ
  • Edmundo de Souza e Silva UFRJ
  • Rosa M. M. Leão UFRJ


Ataques DDoS têm causado prejuízos consideráveis ao longo dos anos. Para mitigar seu impacto, a detecção deve ocorrer preferencialmente próximo à origem. Propomos neste trabalho um sistema leve de detecção de DDoS que usa apenas contadores de bytes e pacotes de roteadores domésticos. Para detectar ataques com informações limitadas, empregamos duas camadas: (1) um classificador treinado com dados reais de usuários domésticos; (2) um modelo hierárquico bayesiano que correlaciona alarmes de várias residências. Usamos código-fonte de malwares reais para gerar tráfego de ataque DDoS nas casas de um grupo de voluntários durante 31 dias. Os experimentos realizados em campo mostraram que nosso sistema possui excelente desempenho.


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MENDONÇA, Gabriel; SANTOS, Gustavo H. A.; SOUZA E SILVA, Edmundo de; LEÃO, Rosa M. M.. Detecção de ataques DDoS usando correlação espaço-temporal bayesiana. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 40. , 2022, Fortaleza. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 224-237. ISSN 2177-9384. DOI: