XGBoost Applied to Identify Malicious Domains Using Passive DNS

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorBrazilian Network Information Center-
Autor(es): dc.creatorSilveira, Marcos Rogerio [UNESP]-
Autor(es): dc.creatorDa Silva, Leandro Marcos [UNESP]-
Autor(es): dc.creatorCansian, Adriano Mauro [UNESP]-
Autor(es): dc.creatorKobayashi, Hugo Koji-
Data de aceite: dc.date.accessioned2022-02-22T00:53:08Z-
Data de disponibilização: dc.date.available2022-02-22T00:53:08Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2020-11-23-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/NCA51143.2020.9306704-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/208337-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/208337-
Descrição: dc.descriptionThe Domain Name System (DNS) is an essential component for the Internet, as its main function is to map the domain name to Internet Protocol addresses, in which the hosts respond. Because of its importance, attackers use this tool for malicious purposes such as spreading malware, botnets, fast-flux domains, and Domain Generation Algorithms (DGAs). In this paper, we present an approach to automatically detect malicious domains using passive DNS, using the supervised machine learning algorithm Extreme Gradient Boosting (XGBoost). We use 12 features extracted exclusively from DNS traffic. The model's evaluation proved its effectiveness with an average AUC of 0.9763.-
Descrição: dc.descriptionUniversidade Estadual Paulista (UNESP)-
Descrição: dc.descriptionNICBR Brazilian Network Information Center-
Descrição: dc.descriptionUniversidade Estadual Paulista (UNESP)-
Idioma: dc.languageen-
Relação: dc.relation2020 IEEE 19th International Symposium on Network Computing and Applications, NCA 2020-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDomain Name System-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectmalicious domain-
Palavras-chave: dc.subjectpassive DNS-
Título: dc.titleXGBoost Applied to Identify Malicious Domains Using Passive DNS-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.