Quantitative Risk Analysis with Qualitative Statements
Bibliographic record
Abstract
Cybersecurity risk analysis is crucial for orga-nizations to assess, identify, and prioritize possible threats to their systems and assets. Organizations aim to estimate the loss cost in case cybersecurity risks occur to decide the control actions they should invest in. Quantitative risk analysis aids organizations in making well-informed decisions about risk mitigation strategies and resource allocation. Therefore, organizations must use quantitative risk analysis methods to identify and prioritize risks rather than relying on qualitative methods. This paper proposes a spreadsheet-based quantitative risk analysis method based on verbal likelihoods. Our approach relies on tables constructed by experts that map between linguistic likelihood and possible probability ranges. Using linguistic terms to estimate the probability of risk occurrence will help experts apply quantitative estimation easily by using common language as input, thus eliminating the need to assign precise probabilities. We experimented with real examples to validate our approach's accuracy and reliability and compared our results with those obtained from another method. Also, we conducted tests to measure our model's performance and robustness. Our study showcases the effectiveness of our approach and demonstrates its potential for risk analysts to use it in real-world applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".