DNS key technologies based on machine learning and network data mining

Xiaofei Liu , Xiang Zhang , Mostafa Habibi

Abstract

Domain Name Systems (DNS) provide critical performance in directing Internet traffic. It is a significant duty of DNS service providers to protect DNS servers from bandwidth attacks. Data mining techniques may identify different trends in detecting anomalies, but these approaches are insufficient to provide adequate methods for querying traffic data in significant network environments. The patterns can enable the providers of DNS services to find anomalies. Accordingly, this research has used a new approach to find the anomalies using the Neural Network (NN) because intrusion detection techniques or conventional rule-based anomaly are insufficient to detect general DNS anomalies using multi-enterprise network traffic data obtained from network traffic data (from different organizations). NN was developed, and its results were measured to determine the best performance in anomaly detection using DNS query data. Going through the R2 results, it was found that NN could satisfactorily perform the DNS anomaly detection process. Based on the results, the security weaknesses and problems related to unpredictable matters could be practically distinguished, and many could be avoided in advance. Based on the R2 results, the NN could perform remarkably well in general DNS anomaly detection processing in this study.

Key Words

data management; domain name system; internet traffic; machine learning; neural network; security

Address

(1) Xiaofei Liu — School of Computer and Information, Anqing Normal University, Anqing 246133, Anhui, China
(2) Xiang Zhang — School of Information Engineering, Jingdezhen University, Jingdezhen 333000, China
(3) Mostafa Habibi — Universidad UTE, Facultad de Arquitectura y Urbanismo, Calle Rumipamba S/N y Bourgeois, Quito 170147, Ecuador
(4) Mostafa Habibi — Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai 600 077, India
(5) Mostafa Habibi — Department of Mechanical Engineering, Faculty of Engineering, Haliç University, 34060, Istanbul, Turkey
(6) Mostafa Habibi — Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.

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