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      COVID-19 malicious domain names classification

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          Abstract

          Due to the rapid technological advances that have been made over the years, more people are changing their way of living from traditional ways of doing business to those featuring greater use of electronic resources. This transition has attracted (and continues to attract) the attention of cybercriminals, referred to in this article as “attackers”, who make use of the structure of the Internet to commit cybercrimes, such as phishing, in order to trick users into revealing sensitive data, including personal information, banking and credit card details, IDs, passwords, and more important information via replicas of legitimate websites of trusted organizations. In our digital society, the COVID-19 pandemic represents an unprecedented situation. As a result, many individuals were left vulnerable to cyberattacks while attempting to gather credible information about this alarming situation. Unfortunately, by taking advantage of this situation, specific attacks associated with the pandemic dramatically increased. Regrettably, cyberattacks do not appear to be abating. For this reason, cyber-security corporations and researchers must constantly develop effective and innovative solutions to tackle this growing issue. Although several anti-phishing approaches are already in use, such as the use of blacklists, visuals, heuristics, and other protective solutions, they cannot efficiently prevent imminent phishing attacks. In this paper, we propose machine learning models that use a limited number of features to classify COVID-19-related domain names as either malicious or legitimate. Our primary results show that a small set of carefully extracted lexical features, from domain names, can allow models to yield high scores; additionally, the number of subdomain levels as a feature can have a large influence on the predictions.

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          Random Forests

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            A Coefficient of Agreement for Nominal Scales

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                Author and article information

                Journal
                Expert Syst Appl
                Expert Syst Appl
                Expert Systems with Applications
                The Author(s). Published by Elsevier Ltd.
                0957-4174
                0957-4174
                20 May 2022
                20 May 2022
                Affiliations
                [1]School of Electrical Engineering and Computer Science (EECS), University of Ottawa, Ottawa, ON K1N 6N5, Canada
                Author notes
                [* ]Corresponding author.
                Article
                S0957-4174(22)00871-5 117553
                10.1016/j.eswa.2022.117553
                9119958
                7ee27aa8-b18b-4dad-8b03-0c03c8a4dfe9
                © 2022 The Author(s)

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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                Article

                machine learning,cybersecurity,phishing attacks,supervised learning,hoeffding trees,online learning

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