Does domain highlighting help people identify phishing sites?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Phishers are fraudsters that mimic legitimate websites to steal user's credenfitial information and exploit that information for identity theft and other criminal activities. Various anti-phishing techniques attempt to mitigate such attacks. Domain highlighting is one such approach recently incorporated by several popular web browsers. The idea is simple: the domain name of an address is highlighted in the address bar, so that users can inspect it to determine a web site's legitimacy. Our research asks a basic question: how well does domain highlighting work? To answer this, we showed 22 participants 16 web pages typical of those targeted for phishing attacks, where participants had to determine the page's legitimacy. In the first round, they judged the page's legitimacy by whatever means they chose. In the second round, they were directed specifically to look at the address bar. We found that participants fell into 3 types in terms of how they determined the legitimacy of a web page; while domain highlighting was somewhat effective for one user type, it was much less effective for others. We conclude that domain highlighting, while providing some benefit, cannot be relied upon as the sole method to prevent phishing attacks.
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.
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 | 0.000 | 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 | 0.000 | 0.001 |
| 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 it