Outlining comprehensive security analysis of a critical infrastructure network
Bibliographic record
Abstract
This paper outlines a security assessment methodology for analysing critical infrastructure networks.The focus is on intentional attacks against critical infrastructure, but otherwise the scope is not delimited much.Comprehensive security analysis of a critical infrastructure network requires an assessment of the probability of an attack, the probability of success of the attack, the propagation of the consequences in the network and the severity of the consequences.In this paper, a critical infrastructure network should be understood as a network including different infrastructures, such as gas, water and electricity.The aim is that the interconnections between different infrastructures are built in the risk model.In the outlined methodology, the analysis starts with the identification of potential attackers and targets, and selection of analysis cases.Then, a network model is utilised to identify attack locations and assess consequences, and in the last steps, attack events and their probabilities are analysed.Although different steps of the methodology can use different risk analysis methods, they are linked so that dependencies between them can be taken into account, and total risk estimates can be determined.It is not specified which particular method should be used in each step, but some potential methods are discussed.The selection of methods can depend on the application target and the size of the problem.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".