Precision Email Simulator for Research on Safety-Critical Phishing Behaviour
Bibliographic record
Abstract
Email is ubiquitous, and in the context of phishing, it becomes critical, as risky behaviours like clicking on phishing links or downloading malicious files can lead to severe consequences.While much research exists on phishing susceptibility, there is still a gap in understanding factors that influence user micro-behaviour when interacting with phishing emails.To address this, we offer a tool, the Precision Email Simulator, to support phishing researchers, as well as considerations in conceptualising controlled 'experimental simulation' studies, which are currently underutilised in phishing research.The Precision Email Simulator simulates real-world email inboxes and tracks precision user data, such as time spent on messages and eye-tracking for key areas like URLs and sender addresses.We discuss the practical uses of our simulator, and provide recommendations and guidelines of using our email simulator. CCS Concepts Security and privacy Phishing; Human and societal aspects of security and privacy.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".