Abstract

Every electronic message poses some threat of being a phishing attack. If recipients underestimate that threat, they expose themselves, and those connected to them, to identity theft, ransom, malware, or worse. If recipients overestimate that threat, then they incur needless costs, perhaps reducing their willingness and ability to respond over time. In two experiments, we examined the appropriateness of individuals’ confidence in their judgments of whether email messages were legitimate or phishing, using calibration and resolution as metacognition metrics. Both experiments found that participants had reasonable calibration but poor resolution, reflecting a weak correlation between their confidence and knowledge. These patterns differed for legitimate and phishing emails, with participants being better calibrated for legitimate emails, except when expressing complete confidence in their judgments, but consistently overconfident for phishing emails. The second experiment compared performance on the laboratory task with individuals’ actual vulnerability, and found that participants with better resolution were less likely to have malicious files on their home computers. That comparison raised general questions about the design of anti-phishing training and of providing feedback essential to self-regulated learning.

Highlights

  • Phishing attacks seek to trick recipients into believing that an email is legitimate, in order to solicit sensitive information or install malware

  • The present analysis reports metacognition metrics and investigates the relationship between metacognition and individual differences as well as real-world vulnerability

  • Exploratory work suggests that those skills are critical to acquiring digital literacy (Greene et al 2014). These results suggest that anti-phishing training may benefit from a focus on metacognitive outcomes, rather than just performance outcomes

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Summary

Introduction

Phishing attacks seek to trick recipients into believing that an email is legitimate, in order to solicit sensitive information (e.g. usernames, passwords, credit card numbers) or install malware. Spear phishing attacks use personal information (e.g. known contacts, industry language, victims’ names) to create more realistic and persuasive messages. In 2018, schools and universities were the third most popular target for social engineering attacks using electronic media, after the public sector and healthcare industries (Verizon 2018). Educational institutions may be at higher risk because of the relative transparency of contact information, job roles, and names for members of their communities. 96% of social engineering attacks, which include phishing and pretexting, are via email (Verizon 2018). Given the difficulty of screening many such messages automatically, human behavior plays a major role in determining the vulnerability (Boyce et al 2011; Cranor 2008; Proctor and Chen 2015; Werlinger et al 2009)

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