ADVERT: An Adaptive and Data-Driven Attention Enhancement Mechanism for Phishing Prevention
Linan Huang, Shumeng Jia, Emily Balcetis, and Quanyan Zhu

TL;DR
ADVERT is a real-time, adaptive visual aid system that leverages eye-tracking data to enhance user attention and improve phishing email recognition accuracy, demonstrating significant effectiveness and robustness in experimental settings.
Contribution
This work introduces ADVERT, a novel data-driven, adaptive attention enhancement mechanism that modifies human attention processes to prevent phishing attacks.
Findings
Increases phishing recognition accuracy from 74.6% to 86%
Meta-adaptation further improves accuracy to over 91%
System is effective, efficient, and robust in real-world experiments
Abstract
Attacks exploiting the innate and the acquired vulnerabilities of human users have posed severe threats to cybersecurity. This work proposes ADVERT, a human-technical solution that generates adaptive visual aids in real-time to prevent users from inadvertence and reduce their susceptibility to phishing attacks. Based on the eye-tracking data, we extract visual states and attention states as system-level sufficient statistics to characterize the user's visual behaviors and attention status. By adopting a data-driven approach and two learning feedback of different time scales, this work lays out a theoretical foundation to analyze, evaluate, and particularly modify humans' attention processes while they vet and recognize phishing emails. We corroborate the effectiveness, efficiency, and robustness of ADVERT through a case study based on the data set collected from human subject…
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