Actas de congresos
XGBoost Applied to Identify Malicious Domains Using Passive DNS
Fecha
2020-01-01Registro en:
2020 Ieee 19th International Symposium On Network Computing And Applications (nca). New York: Ieee, 4 p., 2020.
2643-7910
WOS:000661912700045
Autor
Universidade Estadual Paulista (UNESP)
Brazilian Network Informat Ctr NICBR
Institución
Resumen
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.