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The Geometry of Suspicion: Visual Exploration Patterns in Email Phishing Detection
Francesco Di Nocera1, Lorenzo Arciulo1, Giorgia Tempestini1
1Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, 00196 Rome, Italy.
Journal of Eye Movement Research
|June 25, 2026
Summary
Detecting phishing emails involves broader visual scanning for suspicious messages. Correctly identified phishing attempts show more dispersed eye-fixation patterns than errors, regardless of cybersecurity awareness.
Area of Science:
- Cognitive Psychology
- Cybersecurity
- Human-Computer Interaction
Background:
- Phishing email detection relies on user vigilance and visual scanning.
- Traditional eye-tracking measures (AOI-based) may not capture global visual exploration patterns.
- The Nearest Neighbor Index (NNI) offers a novel approach to analyze fixation distribution.
Purpose of the Study:
- To investigate visual exploration strategies during phishing email detection.
- To integrate AOI-based eye-tracking with the NNI for a comprehensive analysis of gaze patterns.
- To determine how fixation distribution relates to detection accuracy and decision time.
Main Methods:
- Participants (n=30) performed an email classification task (authentic vs. phishing).
- Eye-tracking data were collected, analyzing Area of Interest (AOI) metrics and the Nearest Neighbor Index (NNI).
- Signal Detection Theory (SDT) outcomes and decision times were correlated with eye-tracking measures, controlling for cybersecurity awareness (CAIN).
Main Results:
- Suspicious emails elicited broader visual exploration patterns (higher NNI) compared to non-suspicious emails.
- Correct detections (hits) were associated with more dispersed and regular fixation patterns (higher NNI).
- Incorrect detections (false alarms) were linked to more clustered scanning patterns (lower NNI); decision time was faster for correct responses.
Conclusions:
- The NNI effectively captures global visual exploration differences in phishing detection.
- Phishing detection accuracy is associated with distinct fixation spatial organizations, independent of cybersecurity knowledge.
- Visual exploration patterns, analyzed via NNI, offer insights into cognitive processes during cybersecurity tasks.
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