Detection of Malicious Domains Using Passive DNS with XGBoost

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorNIC.BR-
Autor(es): dc.creatorSilveira, Marcos Rogério [UNESP]-
Autor(es): dc.creatorCansian, Adriano Mauro [UNESP]-
Autor(es): dc.creatorKobayashi, Hugo Koji-
Data de aceite: dc.date.accessioned2022-02-22T00:53:01Z-
Data de disponibilização: dc.date.available2022-02-22T00:53:01Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2020-11-08-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ISI49825.2020.9280552-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/208294-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/208294-
Descrição: dc.descriptionThe Domain Name System (DNS) has as its main function the mapping of domain names to IPs and vice versa. Because of its function combined with the exponential growth of the internet, it has become an essential component. Because of this, attackers use DNS for malicious activities, such as Phishing, Fast-Flux Domains, DGAs, in addition to the spread of malware. In this paper we present an approach for automatic detection of malicious domains using a Passive DNS dataset combined with machine learning techniques. One way to perform the detection of these malicious domains is by blocklists, which can take some time before someone reports and there is human analysis. The model presented in this work is capable of detecting malicious domains at an early stage through its Passive DNS traffic. 12 features were extracted exclusively from DNS traffic. Our model makes use of the XGBoost supervised machine learning algorithm, and obtains an average AUC of 0.976.-
Descrição: dc.descriptionUniversidade Estadual Paulista Unesp-
Descrição: dc.descriptionBrazilian Network Information Center NIC.BR-
Descrição: dc.descriptionUniversidade Estadual Paulista Unesp-
Idioma: dc.languageen-
Relação: dc.relationProceedings - 2020 IEEE International Conference on Intelligence and Security Informatics, ISI 2020-
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Título: dc.titleDetection of Malicious Domains Using Passive DNS with XGBoost-
Aparece nas coleções:Repositório Institucional - Unesp

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