Building a Cybersecurity Culture in Higher Education: Proposing a Cybersecurity Awareness Paradigm
Bibliographic record
Abstract
Today, the world is experiencing constant technological evolution, allowing cyberattacks to manifest through different vectors and widely impacting victims, from specific users to serious damage to institutions’ integrity. Research has shown that a significant percentage of recorded cyber incidents are attributed to social engineering practices or human error. In response to this growing threat, reinforcing cybersecurity awareness among users has become an urgent strategy to develop and apply. However, addressing cybersecurity awareness is a difficult challenge, specifically in the HE industry, where cybersecurity awareness should be an essential part of this type of institution due to the amount of critical data it handles. In addition to the need to strengthen the preparation of new professionals, statistics have shown a significant increase in successful security attacks in this industry. Therefore, this study proposes a conceptual Cybersecurity Awareness and Training Framework for Higher Education to facilitate the establishment of systems that improve the cybersecurity awareness of students in any academic institution, extending to all audiences that coexist in it. This framework encompasses key components intended to continually improve the development, integration, delivery, and evaluation of cybersecurity knowledge for individuals directly or indirectly related to the institution’s information assets.
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 | 0.000 | 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 | 0.001 | 0.008 |
| 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 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".