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- W4383550112 abstract "Email phishing is a major source of fraud costing individuals and organizations billions of dollars each year. As phishing attacks increase in sophistication, there is a growing need to understand the psychology of phishing susceptibility - what makes someone susceptible to phishing and can their susceptibility be predicted? To address these questions, here we take a data-driven approach, fitting a series of neural network models to human behavior in a phishing email detection task. By varying the size of the hidden layers in these networks we investigate the dimensionality of phishing susceptibility - asking how many features of a participant and an email are needed to predict phishing email detection. This analysis suggests that phishing susceptibility is low dimensional, with as few as five dimensions capturing almost half the variability in the data. By further augmenting our networks with inputs from GPT-3, in which email sender, subject line, and preview text are embedded into a high dimensional space, we further show that phishing susceptibility can be predicted for new (untrained) emails. We therefore conclude that phishing susceptibility is low dimensional and moderately predictable and discuss implications of these findings for interventions designed to improve phishing detection." @default.
- W4383550112 created "2023-07-08" @default.
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- W4383550112 date "2023-07-06" @default.
- W4383550112 modified "2023-10-18" @default.
- W4383550112 title "On the dimensionality and predictability of phishing susceptibility" @default.
- W4383550112 doi "https://doi.org/10.31219/osf.io/2e9u4" @default.
- W4383550112 hasPublicationYear "2023" @default.
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