SoK: Human-centered Phishing Susceptibility
Bibliographic record
Abstract
Phishing is recognized as a serious threat to organizations and individuals. While there have been significant technical advances in blocking phishing attacks, end-users remain the last line of defence after phishing emails reach their email inboxes. Most of the existing literature on this subject has focused on the technical aspects related to phishing. The factors that cause humans to be susceptible to phishing attacks are still not well-understood. To fill this gap, we reviewed the available literature and systematically categorized the phishing susceptibility variables studied. We classify variables based on their temporal scope, which led us to propose a three-stage Phishing Susceptibility Model (PSM) for explaining how humans are vulnerable to phishing attacks. This model reveals several research gaps that need to be addressed to understand and improve protection against phishing susceptibility. Our review also systematizes existing studies by their sample size and generalizability and further suggests a practical impact assessment of the value of studying variables: Some more easily lead to improvements than others. We believe that this article can provide guidelines for future phishing susceptibility research to improve experiment design and the quality of findings.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".