Fortifying Critical Infrastructures: Secure Data Management with Edge Computing
Bibliographic record
Abstract
Critical infrastructures (CIs), including energy, healthcare, and transportation, are vital to societal functions, making their security paramount. The emergence of edge computing as a means of safe data management is a direct result of the growing dependence of these infrastructures on real-time data and networked devices. Computing at the edge, or near the source of data, improves efficiency, simplifies data processing, and enables better real-time judgements by decentralising data processing. However, this distributed architecture introduces new security challenges, such as managing a broader attack surface and ensuring data integrity. This paper reviews the role of edge computing in securing critical infrastructures and discusses advanced security measures like encryption, access control, AI-driven anomaly detection, and blockchain. It also outlines future research directions, emphasizing the need for scalable, interoperable edge systems, AI-enhanced security models, quantum-safe encryption, and privacy-preserving techniques. Global standardization is highlighted as essential for consistent, reliable integration. Ultimately, edge computing offers a promising pathway to fortify critical infrastructures against evolving cyber threats, ensuring their continued, resilient operation in an increasingly connected and digital world
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.004 |
| 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".