Exploring the emerging role of large language models in smart grid cybersecurity: a survey of attacks, detection mechanisms, and mitigation strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smart grids are modernizing the future of providing energy for everyone, allowing us to increase the efficiency of power generation, transmission, or distribution using information and communication technologies. However, the network structure of smart grids makes them vulnerable to varying levels of cyber threats. This paper provides a broad overview of cyber threats against smart grids, considering attack surfaces, communication network layers, and the core security triad of confidentiality, integrity, and availability. This survey also outlines emerging threats and covers current protection, prevention, detection, mitigation, and recovery measures, focusing on emerging technologies such as artificial intelligence and large language models (LLMs) in smart grid security. We analyze and show how previous work has tackled and approached similar themes in this area. Amongst our contributions are categorizing the critical parts of smart grids that are most vulnerable to attack, several threat taxonomies, and a review of the increasing importance of LLMs for enhancing grid security. This evaluation underscores the need for effective and robust security technologies to avoid the compromises that result from more sophisticated cyber attacks.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it