Bibliographic record
Abstract
Abstract Power networks are among the most critical infrastructures of any society, since other infrastructures are dependent on power and energy systems. Recently, there has been a trend toward harnessing Information Technology (IT) in power networks to enhance their performance and improve their consumer centricity. This movement, however, has exposed power systems to cyberattacks. Among various types of cyber intrusions, False Data Injection Attack (FDIA) is the most important one, since it stealthily intercepts the legitimate traffic and/or injects malicious data into the system, thus resulting in severe physical and economic damages. There has been a substantial increase in the number of reported FDIAs in recent years, which indicates the necessity of extensive research on vulnerabilities of power systems to FDIAs, probable attack models, impacts of such intrusions, and the possible preventive and detective measures. On this basis, this article presents a comprehensive review of FDIAs in power networks, with a particular focus on attack targets and adversarial methods. Additionally, it elaborates on various types of FDIAs, their implementation steps and strategies, and the vulnerabilities of various components and schemes to this type of attack. Finally, the it briefly discusses FDIA detection techniques in power 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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".