A Strong Three-Factor Authentication Device: Trusted DAVE and the New Generic Content-Based Information Security (CBIS) Architecture
Bibliographic record
Abstract
This report has three objectives. The first objective is to provide a description/analysis of the Trusted DAVE activity performed by DRDC Ottawa and its contractors. The second is to describe different systems where the demonstrator produced under this activity could be used. The last is to analyse, study, and compare different types of network/system architectures. The activity involved the development of three elements: A secure design for a three-factor Trusted Device for Authentication and VErification (Trusted DAVE), a device demonstrator implementing some of those design elements, and an authentication and verification demonstration system that utilises the device demonstrator. The purpose of the device is to provide the user interface component to be used as a part of a strong Verification and Authentication (V&A) capability for systems used to process classified or sensitive data. Four possible systems that could use Trusted DAVE are presented. Two of them are related to the CBIS (Content-Based Information Security) concepts and one integrates CBIS and Kerberos. Finally, three architectures for network systems are presented with their advantages and their limitations. A Generic CBIS architecture covering the one specified in the US CBIS ACTD is defined and compared with the two others. The purpose of the Generic CBIS architecture is threefold: (1) provide an architecture for systems generalizing the US ACTD one, (2) illustrate the architecture's fundamental aspects, and (3) introduce an architecture where Trusted DAVE could be useful.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".