Implementation of the Cybersecurity Incident Severity Scale (CISS) to Assess Cyber Incidents in the Construction Sector
Bibliographic record
Abstract
As the construction industry adopts digital technologies, cybersecurity risks are rising.However, the absence of a standardized incident reporting framework has resulted in limited disclosure of cybersecurity incidents and a lack of a centralized database.This prevents construction companies from learning from past events and developing effective cyber risk management strategies.To address this issue, this study applies the Cybersecurity Incident Severity Scale (CISS) model to assess the severity of cyber incidents within the construction sector.The CISS model uses a structured, semi-quantifiable approach to generate an integrated score, evaluating dimensions such as safety and financial impacts to provide a comprehensive understanding of an incident's consequences.A real-world construction cyber incident serves as a case study to demonstrate the model's applicability.By promoting standardized reporting, the CISS model can improve data collection, ensure consistent reporting across organizations, and enable meaningful comparisons over time and across regions.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".