Real-time Protection Against Microgrid False Data Injection Attacks Using Passive Monitoring
Bibliographic record
Abstract
Microgrids are continuing to play a big role in the smart grids by offering higher reliability and integration of Distributed Energy Resources (DERs). Due to their dependence on accurate and authenticated data, microgrids subjected to False Data Injection (FDI) attacks can have their ability to provide energy negatively impacted. In this paper, a real-time centralized monitoring scheme is proposed to detect and mitigate FDI attacks that falsely portray a line fault. Passive monitoring devices are deployed in multiple regions of the microgrid, and data-driven neural network techniques are used to compute the extent that line measurement inferred from incoming IEC 61850 Sampled Value (SV) frames deviate from expected operation. The deviations are reported to a central security manager, which determines whether or not an FDI attack is underway based on the amount of inconsistency in the reported deviations. The impact of FDI attacks that present the microgrid as being under fault is demonstrated on a microgrid co-simulation testbed. The proposed centralized detection approach is found to be effective at identifying fault FDI attacks in cases where a minority subset of sensors is targeted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".