Uma Análise de Métodos de Seleção de Características aplicados à Detecção de Malwares Android

  • Tainá Soares UNIPAMPA
  • Diego Kreutz UNIPAMPA
  • Vanderson Rocha UFAM
  • Estevão Costa UFAM
  • Luiza Leão UFAM
  • Jonas Pontes UFAM
  • Joner Assolin UFAM
  • Gustavo Rodrigues Combate à Fraude
  • Eduardo Feitosa UFAM

Resumo


A detecção de malwares Android requer tipicamente o treinamento de modelos de aprendizado de máquina utilizando datasets que contém números expressivos de amostras (e.g., 100k, 1M) e características (e.g., 3k, 500k). Para reduzir a dimensionalidade dos datasets, pesquisadores vêm recorrentemente propondo diferentes métodos para a seleção de características (e.g., permissões, chamadas de API). Neste trabalho, avaliamos quatro métodos de seleção de características (SigPID, SigAPI, RFG, ALR) utilizando três datasets diferentes dos utilizados na avaliação original dos métodos. Os resultados indicam que há uma forte relação entre os datasets e os métodos de seleção, mesmo para tipos específicos de características (e.g., permissões).

Palavras-chave: Seleção de Características, Malwares, Android

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Publicado
12/09/2022
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SOARES, Tainá et al. Uma Análise de Métodos de Seleção de Características aplicados à Detecção de Malwares Android. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 22. , 2022, Santa Maria. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 288-301. DOI: https://doi.org/10.5753/sbseg.2022.225321.

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