Impacto da Redução de Dimensão e Seleção de Atributos na Generalização de Modelos de Detecção de Intrusão

Resumo


Trabalhos anteriores apresentam poucas iniciativas voltadas à melhoria da generalização de modelos de detecção de intrusão em cenários distintos de tráfego de rede. Na busca por soluções para esse problema, destaca-se o uso frequente da técnica de Redução de Dimensão. No entanto, análises comparativas entre essa técnica e outras abordagens semelhantes ainda são escassas. Este trabalho propõe a implementação e a análise comparativa da Redução de Dimensão e da Seleção de Atributos, com o objetivo de avaliar seus impactos como estratégias para melhorar a generalização de modelos de detecção de intrusão baseados em aprendizado de máquina. Os resultados obtidos indicam que ambas podem contribuir no enfrentamento desse desafio.
Palavras-chave: Aprendizado de Máquina, Detecção de Intrusão, Generalização de Modelos, Redução de Dimensão, Seleção de Atributos

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Publicado
19/05/2025
SANTOS, Kelson Carvalho; MIANI, Rodrigo Sanches. Impacto da Redução de Dimensão e Seleção de Atributos na Generalização de Modelos de Detecção de Intrusão. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 43. , 2025, Natal/RN. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2025 . p. 728-741. ISSN 2177-9384. DOI: https://doi.org/10.5753/sbrc.2025.6353.

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