Towards Multi-Level Security for NATO Collective Mission Training â a White Paper:
Bibliographic record
Abstract
Distributed simulation is rapidly becoming a necessity for collective mission training. With missions being joint and combined, we will never fight alone. Thus we need to train together, within and between nations. However, in any such scenario it is likely that some or all of the information may be classified at some level and need protection, be it scenarios, weapon and sensor capabilities or doctrines. In order for simulations to be interactive, one-way approaches such as data diodes will not work. Reclassification of systems using a “system high” approach has proven too complicated and expensive. This raises the need for true multi level security in collective mission training. This is indeed one of the big challenges in realizing the full potential of distributed simulation for defence purposes. As part of the NATO RTO program a new modelling and simulation working group has been formed, MSG-080, to look at this topic. Initial members include Canada, Estonia, France, the Netherlands, Norway, Sweden, UK and the US. A kick-off meeting has taken place in October 2010 and a first round of knowledge exchange has taken place. An early conclusion is that most participating nations have similar requirements. This paper summarizes the starting point for this group, including typical use cases where security solutions are needed, some basics about Multi-Level Security principles as well as a description of a few recent experiments carried out by some participants. Finally it describes some early considerations that were raised during the kick-off. Some examples are the need to obscure system capabilities, the need to support both simulation protocols and IT protocols (VoIP etc), the need for adequate performance and the need to get accreditation offices involved.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".