Trends in Smart Grid Cyber-Physical Security: Components, Threats, and Solutions
Bibliographic record
Abstract
The increasing focus on cyber-physical security in Smart Grids (SGs) has catalyzed a surge in research over recent years. This paper comprehensively reviews SG cyber-physical security advancements, diverging from conventional studies that concentrate on specific attack types. It begins with a structured overview of SGs, delineating their cyber and physical layers and analyzing the key processes: generation, transmission, distribution, and consumption. Subsequent sections critique existing survey studies, identifying gaps and underscoring overlooked aspects in the current literature, particularly concerning the challenges faced. The review progresses to analyze current research trends in SG security, evaluating methodologies across both layers and categorizing them into Machine Learning-based, data-driven, and model-based approaches. The analysis includes a detailed classification of research focused on Control, Monitoring, and Protection across each component and stage of SGs. Additionally, the paper examines emerging cyberattack strategies in SGs that have not been extensively reviewed in existing literature. In conclusion, the paper reflects on significant gaps and challenges in SG cyber-physical security research, underscoring the need for further exploration and innovation in this domain. Thus, this review serves as a critical roadmap for future research, delineating the current state and potential directions in the rapidly evolving field of SG security.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".