Descobrindo perfis de tráfego de usuários: uma abordagem não supervisionada

  • Ananda G. Streit UFRJ
  • Rosa M. M. Leão UFRJ
  • Edmundo de Souza e Silva UFRJ
  • Daniel S. Menasché UFRJ

Resumo

As redes domésticas estão cada vez mais complexas. Portanto, é essencial a elaboração de estratégias inovadoras para caracterizar essa nova demanda. Neste trabalho usamos técnicas não supervisionadas de aprendizado de máquina com o objetivo de entender o perfil de tráfego dos usuários. Em parceria com um ISP, coletamos o tráfego de download e upload de mais de 2.000 roteadores domésticos. Usamos uma técnica de decomposição de tensores (PARAFAC) para extrair fatores relevantes de uso da rede e um algoritmo de clusterização para agrupar usuários com padrões de tráfego diário similares. Para caracterizar o comportamento dos usuários em períodos maiores que um dia, usamos a informação dos clusters e um modelo de Markov oculto.

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Publicado
2019-05-06
Como Citar
STREIT, Ananda G. et al. Descobrindo perfis de tráfego de usuários: uma abordagem não supervisionada. Anais do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), [S.l.], p. 169-182, maio 2019. ISSN 2177-9384. Disponível em: <https://sol.sbc.org.br/index.php/sbrc/article/view/7358>. Acesso em: 18 maio 2024. doi: https://doi.org/10.5753/sbrc.2019.7358.

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