Keychain-Based Signatures for Securing BGP
Bibliographic record
Abstract
As a major component of Internet routing infrastructure, the Border Gateway Protocol (BGP) is vulnerable to malicious attacks. While Secure BGP (S-BGP) provides a comprehensive framework to secure BGP, its high computational cost and low incremental deployment benefits seriously impede its wide usage in practice. Using a lightweight symmetric signature scheme, SPV is much faster than S-BGP. However, the speed boost comes at the price of prohibitively large signatures. Aggregated path authentication reduces the overhead of securing BGP in terms of both time and space, but the speed improvement is still limited by public key computation. In this paper, we propose a keychain-based signature scheme called KC-x. It has low CPU and memory overheads and provides strong incentive for incremental deployment on the Internet. As a generic framework, KC-x has the flexibility of using different signature algorithms, which can even co-exist in a hybrid deployment. We investigate two implementations of KC-x: KC-RSA based on RSA and KC-MT based on Merkle hash tree. Using real BGP workloads, our experimental results show that KC-RSA is as efficient as SAS-V (the most efficient software approach for aggregated path authentication), and KC-MT is even three times faster than SPV with 40% smaller signatures. Through the hybrid deployment of KC-MT and KC-RSA, KC-x can achieve both small signature and high processing rate for BGP speakers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".