BUILDING A CYBER SECURITY CULTURE FOR RESILIENT ORGANIZATIONS AGAINST CYBER ATTACKS
Bibliographic record
Abstract
Cybersecurity has emerged as a critical area requiring 24/7 surveillance, in response to the rapidly increasing frequency of cyber threats. Concurrently, there is a notable amplification in both the allocated budget and the academic interest within this domain. In this cyber risk environment, the success of organizations depends on the weakest link, the human factor. Human errors can be reduced by focusing on the beliefs, values and attitudes guiding employee behavior to protect organizations. In this context, the concept of cybersecurity culture emerges as the key to strengthening cyber resilience in organizations. In this study, the findings obtained from the literature review are presented to determine the definition of cybersecurity culture, its importance and the factors considered important for creating and maintaining this culture. In the study, cybersecurity culture is defined as the set of behaviors formed by beliefs, values and attitudes that shape an organization's approach to cybersecurity. Creating a resilient and sustainable cybersecurity culture is possible by focusing on the human aspects of cybersecurity as much as the technical aspects. Leadership knowledge, skills and abilities, developing cybersecurity awareness throughout the organization, effective communication and acceptance of this transformation as a continuous learning experience are listed among the main factors affecting the cybersecurity culture.
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 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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".