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

arXiv: https://arxiv.org/abs/2106.06907 · HTML: https://mlsanity.com/paper/2106.06907/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 57 references that are in the map.
- First posted on arXiv 13 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [A Dynamic Games Approach to Proactive Defense Strategies against Advanced Persistent Threats in Cyber-Physical Systems](https://mlsanity.com/paper/1906.09687/) (2019)
- [Duplicity Games for Deception Design with an Application to Insider Threat Mitigation](https://mlsanity.com/paper/2006.07942/) (2020)
- [A Game of Drones: Cyber-Physical Security of Time-Critical UAV Applications with Cumulative Prospect Theory Perceptions and Valuations](https://mlsanity.com/paper/1902.03506/) (2019)

## Built on by (most cited first, 6 of 6)

- [Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfare](https://mlsanity.com/paper/2403.10570/) (2024)
- [RADAMS: Resilient and Adaptive Alert and Attention Management Strategy against Informational Denial-of-Service (IDoS) Attacks](https://mlsanity.com/paper/2111.03463/) (2021)
- [LLM-Stackelberg Games: Conjectural Reasoning Equilibria and Their Applications to Spearphishing](https://mlsanity.com/paper/2507.09407/) (2025)
- [ZETAR: Modeling and Computational Design of Strategic and Adaptive Compliance Policies](https://mlsanity.com/paper/2204.02294/) (2022)
- [LLM Semantic Signaling Game and Mechanism Design: Systematic Blindness, Awareness Shaping, and Mindset Dynamics](https://mlsanity.com/paper/2606.29113/) (2026)
- [CaptchaAgent: Verifiable Visual Reasoning for CAPTCHA Solving](https://mlsanity.com/paper/2610.12866/) (2026)

## Abstract (arXiv)

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.