Semi-supervised approach for detecting malicious domains in TLDs in their first query

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorBrazilian Network Information Center (NIC.br)-
Autor(es): dc.creatorSilveira, Marcos Rogério-
Autor(es): dc.creatorCansian, Adriano Mauro-
Autor(es): dc.creatorKobayashi, Hugo Koji-
Data de aceite: dc.date.accessioned2025-08-21T19:56:31Z-
Data de disponibilização: dc.date.available2025-08-21T19:56:31Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2025-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s10207-025-00996-3-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/302495-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/302495-
Descrição: dc.descriptionThe Domain Name System (DNS) is essential for the functioning of the Internet, and due to its importance, malicious users exploit this structure to register domains capable of spreading phishing and malware. This study presents a method to detect malicious domains recently registered in Top-Level Domains (TLDs) based on the first DNS query. The approach is semi-supervised, combining supervised and unsupervised machine learning. We use a combination of two supervised algorithms and clustering for analysis. The results of the models feed into a final classifier, providing a probability of maliciousness for the domain. For the training of supervised models, the data is balanced using a hybrid technique of undersampling and oversampling. The training of the unsupervised model focuses exclusively on malicious domains, creating distinct malicious clusters. The models are evaluated in a real environment and can be updated through a retraining module when necessary. The results indicate an AUC of 0.9620 (± 0.01) and an ACC of 0.91 during training, with notable metrics of ACC 0.88, TPR 0.884, TNR 0.875, FPR 0.124, and FNR 0.110 in the testing phase simulating production. This approach provides a robust solution for the early detection of malicious domains in TLDs.-
Descrição: dc.descriptionFundação para o Desenvolvimento da UNESP (FUNDUNESP)-
Descrição: dc.descriptionSao Paulo State University (UNESP), Cristóvão Colombo, 2265, SP-
Descrição: dc.descriptionBrazilian Network Information Center (NIC.br), Av. das Nações Unidas, 11541, 7th Floor, SP-
Descrição: dc.descriptionSao Paulo State University (UNESP), Cristóvão Colombo, 2265, SP-
Descrição: dc.descriptionFUNDUNESP: 2764/2018-
Idioma: dc.languageen-
Relação: dc.relationInternational Journal of Information Security-
???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-
Palavras-chave: dc.subjectSemi-supervised machine learning-
Título: dc.titleSemi-supervised approach for detecting malicious domains in TLDs in their first query-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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