Uma Nova Abordagem para Detecção de Cabeçalhos SMTP Falsos usando Aprendizado Profundo e Geração de Dados Sintéticos

• Patrick M. Tavares CEFET/RJ
• Dalbert M. Mascarenhas CEFET/RJ

DOI: https://doi.org/10.5753/sbseg.2025.10418

Resumo

Este trabalho propõe uma nova abordagem para a detecção de cabeçalhos de e-mail anômalos, com foco em mensagens de phishing, spam e legítimas. Utilizamos um Perceptron Multicamadas (MLP) para classificação e uma Rede Adversária Generativa com Gradiente Penalizado (WGAN-GP) para geração de dados sintéticos. A técnica Gumbel Softmax é empregada para simular características de conjuntos de dados desbalanceados, e os dados gerados são avaliados por testes estatísticos. O Ray Tune é utilizado para otimização dos hiperparâmetros do modelo. Os resultados demonstram que a abordagem proposta melhora a acurácia e a capacidade de generalização na detecção de ameaças em cabeçalhos de e-mail.

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