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

Linan Huang, Shumeng Jia, Emily Balcetis, Quanyan Zhu

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)2585-2597
Number of pages13
JournalIEEE Transactions on Information Forensics and Security
StatePublished - 2022


  • Attention management
  • Bayesian optimization
  • cybersecurity
  • eye tracking
  • human vulnerability
  • phishing mitigation
  • reinforcement learning

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications


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