RiskISM: A Risk Assessment Tool for Substations
Bibliographic record
Abstract
Securing substations is crucial in ensuring unimpeded operations within the Smart Grid. Accordingly, one such measure that is taken to ensure a substation’s security is risk assessment. Identifying the most critical devices within the substation is crucial in mitigating the substation’s operational downtime during an attack. Also, it is possible to use the outcome of risk assessment as input to trust models. Most risk assessment tools require extensive profiling of the substation and its components as well as being modified to suit a substation under study. In this paper, we present RiskISM, a risk assessment tool for substations that implements certain stages of Interpretive Structural Modeling (ISM). It profiles, from the substation architecture, each substation device’s unique identification, device classification, device type and functional influence to determine its criticality level. The tool’s output is the criticality-based colour-coding of each device and the visualization of the extent of functional influence each device has within the substation. It is this functional influence that determines the extent to which an attack on the device cascades when it is compromised. We believe utility providers can use the tool’s output as input to progressively implement defence mechanisms. We also believe that the output of this tool can be used as input to trust models and we have reserved this for future work.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".