2545 Clemson University, South Carolina, USA.
6243 University of Central Florida, USA.
Hum Factors. 2022 Dec;64(8):1379-1403. doi: 10.1177/0018720821999174. Epub 2021 Apr 9.
The present studies examine how task factors (e.g., email load, phishing prevalence) influence email performance.
Phishing emails are a paramount cybersecurity threat for the modern email user. Research attempting to understand how users are susceptible to phishing attacks has been limited and has not fully explored how task factors (e.g., prevalence, email load) influence accurate detection.
In three experiments, participants classified emails as either legitimate or not legitimate and reported on a variety of other categorizations. The first two experiments examined how email load and phishing prevalence influence phishing detection independently. The third experiment examined the interaction of these two factors to determine whether they have compounding effects. All three experiments utilized individual difference variables to examine how cognitive, behavioral, and personality factors may influence classifications.
Experiment 1 suggests that high email load can make the task appear more challenging. Experiment 2 indicates that low phishing prevalence can decrease sensitivity for phishing emails. Experiment 3 demonstrates that high levels of email load can decrease classification accuracy under 50/50 prevalence rates. Notably, performance was poor across all experiments, with phishing detection near chance levels and low discriminability for emails. Participants demonstrated poor metacognition with over confidence, low self-reported difficulty, and low perceived threat for the emails.
Overall, the present studies suggest that high email load and low phishing prevalence can influence email classifications.
Organizations and researchers should consider the influences of both email load and phishing prevalence when implementing phishing interventions.
本研究探讨任务因素(例如,电子邮件负载、网络钓鱼的流行程度)如何影响电子邮件的使用效果。
网络钓鱼电子邮件是现代电子邮件用户面临的主要网络安全威胁。试图了解用户如何容易受到网络钓鱼攻击的研究有限,并且尚未充分探索任务因素(例如,流行程度、电子邮件负载)如何影响准确检测。
在三项实验中,参与者将电子邮件分类为合法或不合法,并报告了各种其他分类。前两个实验分别研究了电子邮件负载和网络钓鱼流行程度如何独立影响网络钓鱼检测。第三个实验研究了这两个因素的相互作用,以确定它们是否具有复合效应。所有三个实验都利用个体差异变量来研究认知、行为和人格因素如何影响分类。
实验 1 表明,高电子邮件负载会使任务看起来更具挑战性。实验 2 表明,低网络钓鱼流行度会降低对网络钓鱼电子邮件的敏感性。实验 3 表明,在 50/50 流行率下,高电子邮件负载水平会降低分类准确性。值得注意的是,所有实验的表现都很差,网络钓鱼检测接近机会水平,并且对电子邮件的区分度很低。参与者表现出过度自信、低自我报告难度和低感知威胁的不良元认知。
总体而言,本研究表明,高电子邮件负载和低网络钓鱼流行度会影响电子邮件分类。
当实施网络钓鱼干预措施时,组织和研究人员应考虑电子邮件负载和网络钓鱼流行度的影响。