Cyber security assessment for QUB HPC environment case study
Bibliographic record
Abstract
Case study: Cyber security assessment for QUB HPC environment Beyond certificationCyber security assessment accelerates action to tighten security in the Northern Ireland High-Performance Computing (NI-HPC) Centre.How can you be confident that your high-performance computing (HPC) environment is protected against cyber attack when you can't apply commonly used tools like anti-virus protection?What will reassure researchers (and research funders) that data and research outputs are secure when recognised security certifications aren't applicable?These questions can keep people who manage HPC environments awake at night.And, until recently, they were exercising minds at the NI-HPC Centre, a UK Tier 2 national HPC facility run jointly by Ulster University and Queen's University Belfast, and funded by the Engineering and Physical Sciences Research Council (EPSRC).Researchers at Queen's and Ulster University rely on the facilities, primarily to crunch data for research into chemistry, neuroscience and food security.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".