Multi-target Risk Score Aggregation for Security Evaluation of Network Environments
Bibliographic record
Abstract
Scoring computer systems/networks in terms of specific threats or concerns can enable the comparison of their security level, in a quantitative manner, to facilitate decision making, e.g., mitigation prioritization. The state-of-the-art approaches have mostly focused on scoring the security of a given target, while aggregating scores of multiple systems where each system can be a potential target remains less explored, e.g., whether network A is relatively more secure than network B. In this paper, we take advantage of the well-established attack path representation and use such paths as inter-system influences to derive a risk score of the entire network. We consider the security semantics of various forms of score aggregation, which has not been studied by prior work, and propose to use what we call pairwise path aggregation. We evaluate our approach with a typical fifth Generation (5G) core network, supplemented by evaluations for other network types. The results show that our approach is able to reflect how the overall security varies with multiple factors in common operational scenarios of IT 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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".