Cyber Physical Security of Energy Hubs Using Feasibility Area Estimation
Bibliographic record
Abstract
The deployment of energy hubs created from the integration of power and gas systems mandates the implementation of a robust cybersecurity protocols to protect such energy hubs from being compromised. Stealthy False Data Injection (FDI) attacks, which mask the system operation, have the ability to infiltrate through the FDI detection prevention layer that would adversely affect the system operation on even make the system inoperable. In this paper, a new method is proposed for detection of the cyberattacks that have bypassed the FDI prevention layers. Using healthy historical operating data of the system, a feasibility area representing the normal operation conditions, is estimated for each state variable in the integrated power and gas system. By collecting the main system parameters, such as bus phasor voltages, phasor current, apparent power, node gas pressure, and pipeline gas flow, the FDI is detected. The simulation results indicate the efficacy of the proposed method in detecting stealthy cyberattacks that have not been revealed by the preventive layer.
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 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".