An Assessment of Employee Knowledge, Awareness, Attitude towards Organizational Cybersecurity in Cameroon
Why this work is in the frame
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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.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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