Detection and Prevention of Malicious Data Manipulation Attacks in Microgrids
Bibliographic record
Abstract
Microgrids are vital components of modern smart grid infrastructures, offering flexibility, resilience, and seamless integration of renewable energy through localized generation and consumption. To protect data-in-transit, communication security—through encryption and integrity verification—is crucial, but it is insufficient. Device malfunctions, incorrect configurations, or internal compromise can cause abnormal behavior even when there are no external attackers present. To guarantee data reliability and system stability, anomaly detection must be carried out in tandem with cryptographic protections. In this paper, we model four advanced cyber-physical attack scenarios in microgrids: (1) time synchronization attack, (2) inverter hijacking, (3) load redistribution, and (4) dynamic load alteration. Real-world data sets are used to simulate these attacks and evaluate their effect on grid operations. We suggest a simple probabilistic detection system built on multivariate Gaussian modeling to find these threats-even when encrypted and integrity-protected data seems valid. Operating at the control center following safe packet receipt, this system finds subtle or hidden anomalies starting at the device level. Our findings show the need of integrating data-centric anomaly detection with secure communication protocols and emphasize the performance of our detection system in real-time environments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".