Analysis of impact of trust on Secure Border Gateway Protocol
Bibliographic record
Abstract
Secure Border Gateway Protocol (S-BGP) mandates that upon reception of a BGP UPDATE message, an S-BGP speaker must verify nested signatures of all nodes in the traversed path; and the router should verify the Address Attestation to check if the source has the right to announce the address prefix. Due to several digital signatures required in each UPDATE, there is a high CPU overhead associated with S-BGP. In this paper, we propose a new approach that reduces the burden of validating the AS-path and the address prefix origination. We define a control layer of trusted nodes that is comprised of major Autonomous Systems (ASes) in the network. In this environment, an AS has to verify only the signatures of intermediate ASes between itself and the last trusted node in the AS-path. Similarly, the address prefix is validated only if it was not previously validated by a trusted AS. Using an original analytical model as well as a simulation model, we measured performance metrics of the new proposal. We show that even with small ratio of trusted nodes, the new scheme can significantly reduce the number of verifications required to validate the AS-path and IP prefixes and the number of public keys required by S-BGP.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| 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".