Bibliographic record
Abstract
It is not uncommon that an enterprise application-level business entity in one security domain needs to be engaged in protected information exchanges across unsecured public networks with a number of other entities from separate security domains in an ad-hoc fashion. Neither having a trusted central authority to manage this activity nor a proprietary security handshake protocol designed for particular applications works; the former is often impractical while the latter lacks interoperability and typically cannot be massively deployed. Experience has also shown that proprietary protocols are usually very weak and vulnerable to a variety of attacks. It is desirable to have a standardized protocol for mutual key exchange and authentication that is resistant to a variety of attacks, scalable, and sufficiently flexible to be deployed in a number of different application environments. Unfortunately, no such standard has been developed for enterprise-level applications and services. This paper defines an XML-based key exchange and authentication framework along with a protocol, with concepts such as two-phase negotiation, standardized key exchange templates that resist attack, and public components for Diffie-Hellman exchange borrowed from ISAKMP/IKE (the layer 3 security framework and protocol for VPN). The proposed protocol also leverages the W3C XML encryption and XML signature specifications to allow field-level encryption and signing of KEAML protocol messages, where required
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.021 |
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".