Eyes on the Phish(er): Towards Understanding Users' Email Processing Pattern and Mental Models in Phishing Detection
Bibliographic record
Abstract
Phishing emails typically masquerade themselves as reputable identities to trick people into providing sensitive information and credentials. Despite advancements in cybersecurity, attackers continuously adapt, posing ongoing threats to individuals and organisations. While email users are the last line of defence, they are not always well-prepared to detect phishing emails. This study examines how workload affects susceptibility to phishing, using eye-tracking technology to observe participants' reading patterns and interactions with tailored phishing emails. Incorporating both quantitative and qualitative analysis, we investigate users' attention to two phishing indicators, email sender and hyperlink URLs, and their reasons for assessing the trustworthiness of emails and falling for phishing emails. Our results provide concrete evidence that attention to the email sender can reduce phishing susceptibility. While we found no evidence that attention to the actual URL in the browser influences phishing detection, attention to the text masking links can increase phishing susceptibility. We also highlight how email relevance, familiarity, and visual presentation impact first impressions of email trustworthiness and phishing susceptibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".