Toward measuring network security using attack graphs
Bibliographic record
Abstract
In measuring the overall security of a network, a crucial issue is to correctly compose the measure of individual components. Incorrect compositions may lead to misleading results. For example, a network with less vulnerabilities or a more diversified configuration is not necessarily more secure. To obtain correct compositions of individual measures, we need to first understand the interplay between network components. For example, how vulnerabilities can be combined by attackers in advancing an intrusion. Such an understanding becomes possible with recent advances in modeling network security using attack graphs. Based on our experiences with attack graph analysis, we propose an integrated framework for measuring various aspects of network security. We first outline our principles andmethodologies. We then describe concrete examples to buildintuitions. Finally, we present our formal framework. It is our belief that metrics developed based on the proposed framework will lead to novel quantitative approaches to vulnerability analysis, network hardening, and attack response.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".