Identifying cyber risk factors associated with construction projects
Bibliographic record
Abstract
As construction projects adopt increasingly interconnected digital technologies, their cyber-attack surface expands, making comprehensive cyber risk management essential to prevent incidents, mitigate risks, and minimize potential losses resulting from such attacks. However, the necessary risk factors for this purpose are lacking. Therefore, the study aims to develop a comprehensive set of project-level cyber risk factors tailored to the complexities of construction projects, identified through a systematic and flexible seven-step methodological framework: (1) a literature review of construction and cybersecurity sources to identify initial factors; (2) initial definition of risk categories; (3) internal evaluation and expert input to refine these factors; (4) distribution of a detailed expert questionnaire for rating; (5) expert evaluations through meetings and feedback sessions to enhance validity; (6) elimination of lower-scoring factors; and (7) establishment of quantitative scales for precise risk assessment. The findings include the 32 identified risk factors into five groups: project information, project structure, information technology (IT), operational technology (OT), and management and human aspects. The contributions include providing a set of risk factors that serve as cybersecurity management references and inputs for future quantitative risk assessments, offering a checklist used for proactive risk management, and introducing a framework adaptable for identifying factors of other risks.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".