XGBoost Applied to Identify Malicious Domains Using Passive DNS
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Data
2020-11-24
Autores
Silveira, Marcos Rogerio [UNESP]
Da Silva, Leandro Marcos [UNESP]
Cansian, Adriano Mauro [UNESP]
Kobayashi, Hugo Koji
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Resumo
The 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.
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Domain Name System, machine learning, malicious domain, passive DNS
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2020 IEEE 19th International Symposium on Network Computing and Applications, NCA 2020.