Security of Wide-Area Monitoring, Protection, and Control (WAMPAC) Systems of the Smart Grid: A Survey on Challenges and Opportunities
Bibliographic record
Abstract
The evolution of power generation systems, along with their related increase in complexity, led to the critical necessity of Wide-Area Monitoring, Protection, and Control (WAMPAC) systems in today’s smart grid. Recent developments in smart measurement devices coupled with data communication technologies allow for significant improvements in power systems’ reliability, efficiency, and security. These technological advancements make WAMPAC systems of significant practical interest. However, the geographically distributed nature of such systems increases the potential attack surface and the risk of critical vulnerabilities. Thus, it is of paramount importance to identify the related threats and vulnerabilities as well as the prominent solutions relevant to WAMPAC systems. Consequently, this paper aims to provide a comprehensive review of WAMPAC security aspects along with the state-of-the-art research initiatives addressing such aspects. Specifically, this paper provides critical taxonomies of the cyber-security scope of WAMPAC that guide the accompanying survey of the recent studies on the WAMPAC security domain. As such, this article aims to pave the way for prospective researchers to pursue further studies in areas that require in-depth investigation into the security, reliability, and efficiency of WAMPAC as the backbone of smart grids.
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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".