Detecção de Ataques de Phishing por Meio de Modelos de Linguagem

• Pedro M. M. Souza UECE
• João V. S. Santos UECE
• Antonio M. B. Neto UECE
• Francisco V. J. Nobre UECE
• Alex F. R. Trajano Instituto Atlântico
• Rafael L. Gomes UECE

DOI: https://doi.org/10.5753/sbrc.2026.19297

Resumo

A sofisticação dos ataques de phishing, impulsionada por IA generativa, limita a eficácia de métodos tradicionais baseados em regras. Este trabalho apresenta o VerificAI, um sistema híbrido para detecção em tempo real de phishing em e-mails e SMS, integrando LLMs, SLMs, RAG, validação de URLs e Aprendizado Ativo. O sistema permite o envio de mensagens suspeitas a um chatbot com resposta automatizada e explicável. Experimentos com conjuntos de dados reais comparam modelos em nuvem e locais. Os resultados mostram que a versão VerificAI com LLMs em nuvem atinge até 97% de F1-score, enquanto a versão do VerificAI com SLMs locais oferece desempenho competitivo com menor latência e maior privacidade.

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