Bibliographic record
Abstract
The Border Gateway Protocol (BGP) is an IETF standard inter-domain routing protocol on the Internet. However, it is well known that BGP is vulnerable to a variety of attacks, and that a single misconfigured or malicious BGP speaker could result in large scale service disruption. We first summarize a set of security goals for BGP, and then propose Pretty Secure BGP (ps-BGP) as a new security protocol achieving these goals. psBGP makes use of a centralized trust model for authenticating Autonomous System (AS) numbers, and a decentralized trust model for verifying the propriety of IP prefix origination. We compare psBGP with S-BGP and soBGP, the two leading security proposals for BGP. We believe psBGP trades off the strong security guarantees of S-BGP for presumed-simpler operations, while requiring a different endorsement model: each AS must select a small number (e.g., one or two) of its peers from which to obtain endorsement of its prefix ownership assertions. This work contributes to the ongoing exploration of tradeoffs and balance between security guarantee, operational simplicity, and policies acceptable to the operator community. 1.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".