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dc.contributor.authorSilveira, Marcos Rogério [UNESP]
dc.contributor.authorCansian, Adriano Mauro [UNESP]
dc.contributor.authorKobayashi, Hugo Koji
dc.date.accessioned2021-06-25T11:09:48Z
dc.date.available2021-06-25T11:09:48Z
dc.date.issued2020-11-09
dc.identifierhttp://dx.doi.org/10.1109/ISI49825.2020.9280552
dc.identifier.citationProceedings - 2020 IEEE International Conference on Intelligence and Security Informatics, ISI 2020.
dc.identifier.urihttp://hdl.handle.net/11449/208294
dc.description.abstractThe 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.en
dc.language.isoeng
dc.relation.ispartofProceedings - 2020 IEEE International Conference on Intelligence and Security Informatics, ISI 2020
dc.sourceScopus
dc.titleDetection of Malicious Domains Using Passive DNS with XGBoosten
dc.typeTrabalho apresentado em evento
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionNIC.BR
dc.description.affiliationUniversidade Estadual Paulista Unesp
dc.description.affiliationBrazilian Network Information Center NIC.BR
dc.description.affiliationUnespUniversidade Estadual Paulista Unesp
dc.identifier.doi10.1109/ISI49825.2020.9280552
dc.identifier.scopus2-s2.0-85098951128
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