Cyber incident exercise for safety protection in critical infrastructure
Bibliographic record
Abstract
Many companies, especially those that own critical infrastructure (CI), must prepare processes to cope with serious incidents before they happen.Conventional safety countermeasures already developed a priori to deal with expected problems, such as machinery malfunction, natural disasters and human errors.Field operators also are well trained against such problems.In recent years, however, cyberattacks have emerged as a 'clear and present danger' and have rendered CI uncertain and unsafe through industrial control systems (ICSs).Thus, CI owners should now prepare countermeasures to ensure the safety and security of ICSs.Unfortunately, responding to situations without experience and developing adequate countermeasures is a difficult challenge.A certain resilience must be developed that gives the actors the ability to flexibly cope with a crisis and quickly recover to a safer state.In CI systems, field operators are the most important element for dynamically managing ICS emergency response.In this paper, the authors would like to discuss the following two problems:1. Simultaneous achievement framework of safety and security in ICSs 2. Personnel training methodology based on the above framework Also, we present an illustrative example of the proposed framework and methods based on exercises in which almost 200 CI personnel and security experts participated.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".