Agreement and Divergence in Predicting Human Phishing Classification Decisions

Despite enormous efforts to develop defenses against phishing attacks, humans still struggle to detect phishing emails given the constantly evolving attacker strategies. This paper aims to test the predictive capabilities of a cognitive model that represents the individual susceptibility to phishing emails. We developed an instance-based learning model (IBL) that captures the frequency, recency, and familiarity aspects of decision-making and explores its potential for personalized anti-phishing training. We investigate the same cognitive model using three different methods. At the same time, we compare the performance of these three models to human classification of phishing and ham emails and determine the synchronization rates with human participants in a Phishing Training Task. Our results reveal that using prior human experience and optimizing parameters improves model accuracy. These findings suggest significant advances in modeling human decision-making patterns and cognitive processes, demonstrating strong alignment with human decisions during training.