ADVERT: An Adaptive and Data-Driven Attention Enhancement Mechanism for Phishing Prevention
Linan Huang, Shumeng Jia, Emily Balcetis, Quanyan Zhu
Abstract
Attacks exploiting the <i>innate</i> and the <i>acquired</i> vulnerabilities of human users have posed severe threats to cybersecurity. This work proposes ADVERT, a <i>human-technical solution</i> 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 <i>visual states</i> and <i>attention states</i> 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 <i>analyze</i>, <i>evaluate</i>, and particularly <i>modify</i> humans’ attention processes while they vet and recognize phishing emails. We corroborate the <i>effectiveness</i>, <i>efficiency</i>, and <i>robustness</i> 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.