Cloud-Centric Collaborative Security Service Placement for Advanced Metering Infrastructures
Bibliographic record
Abstract
The extensive integration of smart meters to utility communication networks introduces numerous cyber security concerns. Such meters are the primary access points to a smart grid advanced metering infrastructure (AMI). They are physically unprotected devices that are geographically distributed in low-trust environments. Nonetheless, they are expected to transceive vital information regarding consumption, billing, and load management. The security of such information is an important requirement for operational continuity of a power system. Costs, system complexity, and response time are major considerations in designing security solutions for such cases. It is anticipated that future grids will be powered by the advancement of cloud computing. The security-as-a-service is a model that exploits the potential of cloud computing to provide cyber security solutions. Its key offerings include cost reduction, simplicity, and faster response. This paper proposes a cloud-centric collaborative security service architecture for the monitoring of upstream AMI traffic. It also includes a collaboration-aware service placement scheme for the proposed architecture. The placement scheme develops a quadratic assignment problem that minimizes latency. Case studies demonstrate the enhanced performance of the proposed scheme under various scenarios.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".