Measuring Network Security Using Bayesian Network-Based Attack Graphs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Given the increasing dependence of our societies on information systems, the overall security of these systems should be measured and improved. Existing work generally focuses on measuring individual vulnerabilities instead of measuring their combined effects. Recent research has explored the application of attack graphs and probabilistic security metrics to address this challenge. However, such work usually assumes metrics of individual vulnerabilities to be independently distributed and combines them in an arbitrary manner. They cannot address more realistic cases, such as exploiting one vulnerability makes another vulnerability easier to exploit. In this paper, we propose to model probability metrics based on attack graphs as a special Bayesian Network. This approach provides a sound theoretical foundation to such metrics. It can also provide the capabilities of using conditional probabilities to address the general cases of interdependency between vulnerabilities.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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