Cybersecurity in Construction: Where Do We Stand and How Do We Get Better Prepared
Bibliographic record
Abstract
The architecture, engineering, and construction (AEC) industry is increasingly becoming digital and more prone to cyber-attacks. Although there are several studies and standards in the cybersecurity domain, experts suggest that domain-specific studies need to be conducted to address the unique challenges faced within each of the different industries. Therefore, several cybersecurity studies have been undertaken for various industries, such as healthcare, manufacturing, telecommunication, and energy. However, this type of study is largely missing in the AEC industry due to different reasons, including lack of awareness. To address that, this study aims to (a) compare and analyze the number of cybersecurity-related documents in the AEC industry with several other industries, and (b) extract and analyze the cybersecurity-related documents data to identify potential future research trends and topics for the AEC community. The Web of Science (WOS) database, consisting of significant and influential journal publications, was used for document retrieval. VOSviewer was used to identify key research topics and trends in the cybersecurity domain and define future cybersecurity research in the AEC industry. WOS document retrieval results that compared the total number of publications corroborated the little to no attention received to cybersecurity investigation in the AEC industry. In addition, the VOSviewer analysis revealed three significant areas of research in the cybersecurity community that provide a reasonably justified roadmap for conducting cybersecurity research in the AEC industry. This study could greatly benefit the AEC research community and potential reaping benefits to the industry by creating more awareness among different stakeholders.
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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.025 | 0.035 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".