Detalhes bibliográficos
Ano de defesa: |
2023 |
Autor(a) principal: |
Moura, Marcos Paulo Fernandes de |
Orientador(a): |
Não Informado pela instituição |
Banca de defesa: |
Não Informado pela instituição |
Tipo de documento: |
Dissertação
|
Tipo de acesso: |
Acesso aberto |
Idioma: |
por |
Instituição de defesa: |
Não Informado pela instituição
|
Programa de Pós-Graduação: |
Não Informado pela instituição
|
Departamento: |
Não Informado pela instituição
|
País: |
Não Informado pela instituição
|
Palavras-chave em Português: |
|
Link de acesso: |
http://repositorio.ufc.br/handle/riufc/75981
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Resumo: |
As technology advances, both businesses and governments need to be more concerned about security. With the development of technology, whether in cloud computing, internet of things or artificial intelligence, attackers are able to carry out distributed denial of service attacks DDoS at a lower cost and it also becomes increasingly difficult to detect and prevent these attacks. Today there are already several methods used to detect these attacks, such as Turing test, machine learning methods and an approach focused on modeling normal user behavior profiles. Such methods have a good level of efficiency, however they also have some problems such as interfering with the quality of the user experience or having a high computational cost. In this work, a new mechanism for detecting DDoS attacks at the application layer is proposed using a statistical evaluation technique based on the service’s traffic profile. The obtained results demonstrated that the mechanism has a 1% false positive rate for regular traffic and 0% false negative rate for the scenarios tested. |