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PUBLICATIONS

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

Published

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

Keywords

malicious domain name classification, machine learning, deep learning
Trustworthy networks, Networking, Network security and robustness, Cybersecurity, Artificial intelligence and Advanced communications

Citation

Wang, Z. and Montgomery, D. (2022), Use of Supervised Machine Learning to Detect Abuse of COVID-19 Related Domain Names, Elsevier (Accessed June 29, 2022)

Created April 13, 2022