Assessing awareness of Finnish healthcare personnel against phishing and ransomware attacks
Bibliographic record
Abstract
With ever increasing frequency and impacts of cyber-attacks, cyber security is more needed than ever. Cyber security applies in all domains with networked computers However, depending on the field the consequences that can arise from a successful cyber-attack differ. Equipment downtime due to a cyber-attack in the healthcare sector can lead to situation where patients in critical situation cannot receive the due treatment on time. \n \nThe goal of this thesis is to assess the current awareness of the Finnish social and healthcare personnel against phishing and ransomware attacks. To assess the awareness level, a survey was conducted surveying personnel working in Finnish healthcare companies with 204 respondents participating in the survey. \n \nFindings from the survey identify reveal that a quarter of the respondents never had any kind of cyber security training and out of those 86 % agree that they needed to handle confidential data to perform their work. Additionally, there was a clear agreement that more cyber security training was needed at work. The results also reveal that there is confidence that additional trainings can improve the overall cyber security of the organization.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 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".