Credible-BGP: A Hybrid Cryptosystem to Secure BGP
Bibliographic record
Abstract
BGP is built under the assumption that Autonomous Systems (ASes) are trusted and operate according to the standard. This was quickly revealed to be untrue in the current model of the Internet. Many subsequent protocols were proposed to address the security issues of the BGP protocol. Among them, SBGP offer secure and guaranteed means to distribute route reachability information. However, the assumption under which the protocol is built resulted in a significant computational overhead due to extensive use of cryptographic operations. Indeed, upon the reception of an update, a node has to verify the embedded signature of each node in the AS-PATH in an onion fashion. In this paper, we present a novel approach that reduces the cost of construction and verifications of BGP updates. We make the assumption that some ASes (Like Tier-1 ISPs) can be considered to be trusted by the rest of the ASes. We build a new protocol that employs symmetric and asymmetric cryptosystems to build a secure and efficient mechanism to distribute route information. Based on simulation studies, we noticed considerable reduction of the cost of update construction and verification despite a slight increase of the messages exchanged to reach the steady state.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".