ADVERT: An Adaptive and Data-Driven Attention Enhancement Mechanism for Phishing Prevention

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 experiments conducted at New York University. The results show that the visual aids can statistically increase the attention level and improve the accuracy of phishing recognition from 74.6% to a minimum of 86%. The meta-adaptation can further improve the accuracy to 91.5% (resp. 93.7%) in less than 3 (resp. 50) tuning stages.