Stacking-based Committees para Detecção de Ataques em Redes de Computadores - Uma abordagem por exaustão

  • Thiago J. Lucas UNESP
  • Kelton A. P. da Costa UNESP
  • Eduardo A. Moraes FATEC
  • Paulo R. G. Hernandes Júnior FATEC
  • Miguel J. das Neves FATEC

Resumo


A aplicação de técnicas de Machine Learning na detecção de ataques em redes de computadores tem obtido bons resultados, com destaque para os métodos de Ensemble, que conseguem melhorar o desempenho de classificadores individuais. O estudo foi realizado utilizando o Dataset CICIDS-2017 e considerou a escolha de classificadores com base em uma revisão sistemática da literatura, objetivando encontrar o estado-da-arte e as tendências, tanto para os algoritmos de classificação quanto para as técnicas de ensemble. Committees que reduzem significativamente os erros de classificação são apresentados modelando detectores de intrusão que são superiores aos métodos individuais e aos trabalhos correlatos comparados obtendo acurácia média de 99.92%.

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Publicado
16/08/2021
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LUCAS, Thiago J.; COSTA, Kelton A. P. da; MORAES, Eduardo A.; HERNANDES JÚNIOR, Paulo R. G.; NEVES, Miguel J. das. Stacking-based Committees para Detecção de Ataques em Redes de Computadores - Uma abordagem por exaustão. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 39. , 2021, Uberlândia. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 644-657. ISSN 2177-9384.