Bibliographic record
Abstract
Aims: This paper addresses critical security challenges of the smart power grid due to the tight coupling between cyber and physical systems.It features approaches to inform utilities, policymakers, researchers, and consumers about practices to reduce the risks from cyber attacks.Study Design: The paper introduces key cyber-physical systems and standards of the smart grid; identifies the security threats; and shows the need for protection at various levels of the power grid.A risk-based approach has been proposed, which could be implemented to achieve the vision of a secure smart grid infrastructure.Place and Duration: The study was carried out in Ottawa, Ontario, Canada during the period of January 2012 to January 2013.Methodology: Risk in the context of the smart grid,security threats, priorities, and countermeasures have been presented anda combined framework for risk management that can be translated into continuous security improvement programs has been proposed.Conclusion: The proposed framework calls for a coordinated assessment of cyber and physical risks keeping the whole grid security goals in mind. Integrated risk management approaches are significantly needed to efficiently guide resource allocations and optimize practices at all levels of the smart grid platform to identify the risk, measure it, minimize it, and finally engineer out the risks by the use of available resources, to an acceptable and approved standards.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".