An Assessment of Employee Knowledge, Awareness, Attitude towards Organizational Cybersecurity in Cameroon
Bibliographic record
Abstract
In our increasingly digitized and interconnected society, people are poorly protected against cyberthreats, with the main reason being user behavior. Human behavior and actions are unpredictable in nature and this make human an important element and enabler of cybersecurity. The objective of the study is promotion of adoption of non-technical countermeasures (such as user awareness) for a comprehensive and holistic way to manage cyber security in organizations in Cameroon. We conducted a subjective study to measure the level of employees’ knowledge and general awareness, risky behavior they engage in, and attitude toward various aspects of cybersecurity and cyberthreats to show the need for user education, training, and awareness. For the study described in this paper, a self-report questionnaire was developed and data were collected from 214 participants. The results of a descriptive statistic percentage indicated that less than 50% of respondents have completed or has regular training program. We find that over 61% of the participants do not have sufficient knowledge of their organization cyber security policies. Among other findings, the over 60% of employees’ mistakes or violations of security policy are not disciplined or penalized is a demonstration of lack of legal status of cyber-attacks. Cyber resilience in any organization is a responsibility shared by both management and employees. Proactive human management element that can actively hunt for malicious activity and indicators of compromise is recommended.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".