Use of Supervised Machine Learning to Detect Abuse of COVID-19 Related Domain Names

Published

May 1, 2022

Author(s)

Zheng Wang, Douglas Montgomery

Abstract

A comprehensive evaluation of supervised machine learning models for the COVID-19 related domain name detection is presented. One representative conventional machine learning implementation and nineteen state-of-the-art deep learning implementations are evaluated. The deep learning implementations include the recurrent, convolutional, and hybrid models. The detection rate metrics and the computing time metrics are considered in the evaluation. The result reveals that advanced deep learning models outperform conventional machine learning models in terms of detection rate. And some tradeoff between detection rate and computing speed is needed for the choice of machine learning models.

Citation

Elsevier

Pub Type

Journals

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Keywords

malicious domain name classification, machine learning, deep learning

Citation

Wang, Z. and Montgomery, D. (2022), Use of Supervised Machine Learning to Detect Abuse of COVID-19 Related Domain Names, Elsevier, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932703 (Accessed April 25, 2026)

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