Phishing in a university community: Two large scale phishing experiments
Bibliographic record
Abstract
Phishing is a type of social engineering where a potential victim is sent a message that impersonates a legitimate source or organization. Phishing attacks typically lure the targets into revealing confidential information such as password, credit card details, bank account numbers, or any other sensitive information. Human behavior and technology are two equally important aspects of phishing attacks, while current anti-phishing research have focused on the technology front, very few real life studies have been performed with a focus on the human aspects of phishing attacks. In this paper, we present the results of two large scale real life phishing attacks conducted on more than 10,000 community members of a university that includes students, alumni, faculty and staff. Our study is the first large scale phishing experiment on human subjects. Previous work suggests that users' demographics are useful indicators in identifying the most vulnerable users to phishing attacks. Our results illustrate that user demographics alone cannot predict user's susceptibility to phishing attacks. We also found that warning users about phishing risks alone is not sufficient to prevent more users from responding to the phishing attack. Even though subjects were warned not to respond to phishing emails, many disregarded the warning. We explain our findings through analysis of the empirical results of the two real life phishing attacks conducted.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".