Modeling False Data Injection Attacks in Integrated Electricity-Gas Systems
Bibliographic record
Abstract
Integrated electricity-gas systems (IEGSs) rely heav-ily on communication systems and are vulnerable to cyberattacks. In order to gain insights into intruders behavior and design tailored detection and mitigation methods, this paper studies the modeling of false data injection attacks (FDIAs) on IEGSs. First, we design a tailored static state estimation model and a bad data detection method for IEGSs. Then, we develop FDIAs on IEGSs with complete network topology and parameter information and give conditions for ensuring the stealthiness of these FDIAs. Par-ticularly, the FDIAs consider the cyberattack interdependency caused by power-gas coupling facilities. Next, we develop FDIAs on IEGSs when intruders have only local network topology and parameter information of an IEGS. At last, we explore FDIAs on IEGSs when intruders have only local network topology infor-mation of an IEGS and mathematically prove the existence of FDIAs, specifically targeting gas compressors. Simulation results validate the effectiveness of the proposed FDIAs on IEGSs with both complete and incomplete network information.
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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 | 0.001 | 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 | 0.001 |
| 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".