Construction cybersecurity and critical infrastructure protection: new horizons for Construction 4.0
Bibliographic record
Abstract
One of the key concepts of Construction 4.0 is cyber-physical systems. The construction industry is increasingly creating valuable digital assets, but it is also gradually using digital technology to plan, design, build, monitor, and control the physical ones. This makes construction sites and operations vulnerable to cyber-attacks. While the damage to digital assets can have financial implications, attacks on digitally-controlled physical assets may impact people’s well-being and, in worst-case scenarios, result in casualties. The problem is amplified by the emerging cyber-physical nature of the systems, where the human checks may be left out. The construction industry could draw inspiration from the work done in critical infrastructures (CI). Construction is the prelude of any socio-technical asset tagged as a CI. While most assets may not be critical in the CI sense, they are essential to a business’ operations and the people directly or indirectly associated with them. This study presents a literature review on the previous CI protection (CIP) efforts and construction cybersecurity studies to show their synergy. Recommendations based on well-established CIP processes to make construction more cyber-secure are provided. It is expected that this study will create awareness about cybersecurity practices within the construction industry. Ongoing work includes understanding where construction stands and developing a framework to address cybersecurity throughout the different project phases.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".