Does Domain Highlighting Help People Identify Phishing Sites
Bibliographic record
Abstract
Phishers are fraudsters that mimic legitimate websites to steal user’s credential 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.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".