Factors Affecting Employees' Susceptibility to Cyber-Attacks
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
ABSTRACT We examine factors associated with employees' susceptibility to phishing attacks in a professional services firm and a financial services firm (bank). We measure three dimensions of suspicion (skepticism, suspicion of hostility, and interpersonal trust), and three cognitive traits (risk-taking propensity, cognitive [inhibitory] control, and social cognition), while controlling for demographic and work context factors. We find that these traits interact in complex ways in determining individuals' susceptibility to phishing attacks. Bank employees are more susceptible to being phished than professional services firm employees, but within the bank, the employees with professional certificates are less susceptible to phishing attacks than other bank employees. Also, employees with self-reported responsibility for cybersecurity are less likely to be phished. These findings could be used to create a screening tool for identifying which employees are particularly susceptible to phishing attacks, to tailor training, or redesign jobs to counter those susceptibilities and reduce security risk.
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 | 0.002 | 0.000 |
| 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 | 0.003 |
| 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