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 March 22, 2023)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Use of Supervised Machine Learning to Detect Abuse of COVID-19 Related Domain Names
Published
Author(s)
,
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
Download Paper
Keywords
malicious domain name classification, machine learning, deep learning
Citation
Created May 1, 2022, Updated November 29, 2022