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新冠疫情骗局:新冠疫情期间网络钓鱼攻击及其应对措施调查

The COVID-19 scamdemic: A survey of phishing attacks and their countermeasures during COVID-19.

作者信息

Al-Qahtani Ali F, Cresci Stefano

机构信息

College of Science and Engineering Hamad Bin Khalifa University (HBKU) Doha Qatar.

Institute of Informatics and Telematics (IIT) National Research Council (CNR) Pisa Italy.

出版信息

IET Inf Secur. 2022 Sep;16(5):324-345. doi: 10.1049/ise2.12073. Epub 2022 Jul 4.

DOI:10.1049/ise2.12073
PMID:35942004
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9349804/
Abstract

The COVID-19 pandemic coincided with an equally-threatening scamdemic: a global epidemic of scams and frauds. The unprecedented cybersecurity concerns emerged during the pandemic sparked a torrent of research to investigate cyber-attacks and to propose solutions and countermeasures. Within the scamdemic, phishing was by far the most frequent type of attack. This survey paper reviews, summarises, compares and critically discusses 54 scientific studies and many reports by governmental bodies, security firms and the grey literature that investigated phishing attacks during COVID-19, or that proposed countermeasures against them. Our analysis identifies the main characteristics of the attacks and the main scientific trends for defending against them, thus highlighting current scientific challenges and promising avenues for future research and experimentation.

摘要

新冠疫情同时也伴随着一场同样具有威胁性的诈骗大流行

一场全球范围内的诈骗和欺诈泛滥。疫情期间出现的前所未有的网络安全问题引发了大量研究,以调查网络攻击并提出解决方案和对策。在这场诈骗大流行中,网络钓鱼是迄今为止最常见得攻击类型。这篇综述论文回顾、总结、比较并批判性地讨论了54项科学研究以及政府机构、安全公司的许多报告和灰色文献,这些研究调查了新冠疫情期间的网络钓鱼攻击,或提出了针对这些攻击的对策。我们的分析确定了攻击的主要特征以及防范攻击的主要科学趋势,从而突出了当前的科学挑战以及未来研究和实验中充满希望的途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9abc/9349804/71a2fe7034b9/ISE2-16-324-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9abc/9349804/5663a7eb7fb2/ISE2-16-324-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9abc/9349804/12dcf398fa36/ISE2-16-324-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9abc/9349804/71a2fe7034b9/ISE2-16-324-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9abc/9349804/5663a7eb7fb2/ISE2-16-324-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9abc/9349804/12dcf398fa36/ISE2-16-324-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9abc/9349804/71a2fe7034b9/ISE2-16-324-g002.jpg

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Neural Comput Appl. 2023;35(7):4975-4992. doi: 10.1007/s00521-021-06305-y. Epub 2021 Jul 28.
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