A Hybrid Decision-making Approach to Security Metrics Aggregation in Cloud Environments
Bibliographic record
Abstract
In cybersecurity, being able to quantity the level of security has been a long quest so that decisions can be made toward improving security. Various metrics have been proposed and applied, which can usually be computed from collected measurements. However, only certain aspects of the target system are measured corresponding to the purpose the metrics were designed for, be it software vulnerabilities or configuration errors, thus lacking a concise and clear image of the overall security of a system for the practitioners to act on, especially when it comes to large-scale or complex systems.We argue that overall security metrics are defined by humans based on specific security goals before they can be computed. Therefore, we propose a hybrid approach to the aggregation of well-established individual security metrics by combining machine computation with human decision making. In particular, we modify the Analytic Hierarchy Process (AHP) to reach a group decision of selected “experts”, which can derive the weights of individual metrics for their aggregation. We showcase its feasibility by selecting several common metrics to measure the target systems in our testbed, and conducting an AHP survey with seventeen experts. The resulted overall security score for the target systems shows how our approach enables comparison of the overall security between those systems. By considering cloud-oriented settings, we also showcase how this approach can be applicable to today’s virtualized environments.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".