Evaluating Students’ Vulnerability and Awareness to Phishing Attacks in Educational Institutions
Bibliographic record
Abstract
The rapid growth of the internet has made students increasingly susceptible to phishing attacks, posing risks such as identity theft, financial fraud, and other cybercrimes.Reports indicate that these phishing attacks increasingly target universities and students in higher education institutions (a case study of final engineering students).The objective of this study is to assess the susceptibility and knowledge of students towards phishing attacks through the configuration and analysis of a phishing framework.The methodology encompassed the establishment of a phishing campaign specifically designed to target the academic setting of the students, followed by an assessment of their reactions to the phishing emails.An online survey was conducted to assess students' cyber security comprehension level and their responses to phishing emails.The findings of this study indicate that a significant proportion of students' exhibit susceptibility to phishing attacks and demonstrate a lack of awareness regarding the nature of phishing emails.In conclusion the study enhanced students' awareness of cyber security concerns and equip them with the necessary knowledge to safeguard themselves against phishing attacks in both educational settings and real-world scenarios.
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 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 | 0.001 | 0.001 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".