Cybersecurity Vulnerabilities in Off-Site Construction
Bibliographic record
Abstract
Industry 4.0 is seeking to advance traditional construction practices towards more efficient and internet of things (IoT)-based construction practices, such as offsite construction. Offsite construction (OSC) allows for the simultaneous fabrication of building modules and onsite work. Integrating IoT technologies in construction practice is projected to improve the industry’s growth. However, there is an increase in cybersecurity vulnerabilities. Cyber threats are becoming more disruptive and targeted, resulting in monetary and infrastructure losses. Furthermore, the COVID pandemic and the instability in Europe have seen over 100% increases in cyber-attacks, and most industries have weak cybersecurity protocols. The adoption of cybersecurity frameworks in the construction industry is sluggish, and the existing security frameworks fall short in addressing the needs of the industry. This paper gives a concise review of the offsite construction value chain vulnerabilities. We explore the existing cybersecurity frameworks and identify their limitations. Cybersecurity is presented as one of the most crucial components that has received little or no attention in OSC. The future of OSC is promising with the incorporation of Industry 4.0 technologies; however, its development needs to consider more proactive security approaches and management techniques that are adapted to the current hostile cyber landscape.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".