From Wired to Wireless: Challenges of False Data Injection Attacks Against Smart Grid Sensor Networks
Bibliographic record
Abstract
Existing studies on False Data Injection Attacks (FDIAs), a type of stealth attacks against power grids aimed at compromising the system cyber-physical security, have primarily been conducted on wired systems in which state estimation is represented by overdetermined DC power flow models. The emerging trend of Smart Grid (SG) assisted by the widespread deployment of Wireless Sensor Networks (WSNs) for various new functionalities, on the other hand, calls for a review of how some certain well-established premises need to be adjusted in the new context. In addition to related studies based on traditional bus-based systems, certain broad changes brought by the use of WSNs in grid systems will be introduced in this paper. Subsequently, differences in terms of bad data detection (BDD), false data injection attack strategy or physical feasibility of attack methods caused by the shift of scenario will be compared and briefly analyzed. A summary of new or previously overlooked requirements for FDIA studies will then be appended. By presenting a comprehensive review of related studies we will shed light on potential future research directions.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".