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dc.contributor.authorSilveira, Marcos Rogerio [UNESP]
dc.contributor.authorDa Silva, Leandro Marcos [UNESP]
dc.contributor.authorCansian, Adriano Mauro [UNESP]
dc.contributor.authorKobayashi, Hugo Koji
dc.date.accessioned2021-06-25T11:10:33Z
dc.date.available2021-06-25T11:10:33Z
dc.date.issued2020-11-24
dc.identifierhttp://dx.doi.org/10.1109/NCA51143.2020.9306704
dc.identifier.citation2020 IEEE 19th International Symposium on Network Computing and Applications, NCA 2020.
dc.identifier.urihttp://hdl.handle.net/11449/208337
dc.description.abstractThe 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.en
dc.language.isoeng
dc.relation.ispartof2020 IEEE 19th International Symposium on Network Computing and Applications, NCA 2020
dc.sourceScopus
dc.subjectDomain Name System
dc.subjectmachine learning
dc.subjectmalicious domain
dc.subjectpassive DNS
dc.titleXGBoost Applied to Identify Malicious Domains Using Passive DNSen
dc.typeTrabalho apresentado em evento
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionBrazilian Network Information Center
dc.description.affiliationUniversidade Estadual Paulista (UNESP)
dc.description.affiliationNICBR Brazilian Network Information Center
dc.description.affiliationUnespUniversidade Estadual Paulista (UNESP)
dc.identifier.doi10.1109/NCA51143.2020.9306704
dc.identifier.scopus2-s2.0-85099723851
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