MailTrout:用于检测网络钓鱼电子邮件的机器学习浏览器扩展

P. Boyle, Lynsay A. Shepherd
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引用次数: 3

摘要

COVID-19大流行的爆发导致网络攻击和网络犯罪增加,特别是网络钓鱼企图。与网络钓鱼电子邮件相关的网络犯罪会对受害者造成重大影响,他们可能会遭受金钱损失和身份盗窃。现有的反网络钓鱼工具并不总能捕获所有的网络钓鱼电子邮件,让用户决定电子邮件的合法性。机器学习技术识别重复模式并应对整体变化的能力补充了反网络钓鱼技术的性质,因为网络钓鱼攻击可能在措辞上有所不同,但通常遵循相似的模式。本文介绍了一个名为MailTrout的浏览器扩展,它将机器学习集成在一个可用的安全工具中,以帮助用户检测网络钓鱼电子邮件。MailTrout在检测网络钓鱼邮件时表现出了很高的准确性,并为最终用户提供了很高的可用性。©Boyle等人。BCS学习与发展有限公司出版。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MailTrout: A Machine Learning Browser Extension for Detecting Phishing Emails
The onset of the COVID-19 pandemic has given rise to an increase in cyberattacks and cybercrime, particularly with respect to phishing attempts. Cybercrime associated with phishing emails can significantly impact victims, who may be subjected to monetary loss and identity theft. Existing anti-phishing tools do not always catch all phishing emails, leaving the user to decide the legitimacy of an email. The ability of machine learning technology to identify reoccurring patterns yet cope with overall changes complements the nature of anti-phishing techniques, as phishing attacks may vary in wording but often follow similar patterns. This paper presents a browser extension called MailTrout, which incorporates machine learning within a usable security tool to assist users in detecting phishing emails. MailTrout demonstrated high levels of accuracy when detecting phishing emails and high levels of usability for end-users. © Boyle et al. Published by BCS Learning and Development Ltd.
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