GNSS Time Signal Spoofing Detector for Electrical Substations
Bibliographic record
Abstract
This paper introduces a novel method of GNSS spoofing detection with applications in electrical substations. Time sensitive applications in electricity substations, including Phasor Measurement Units (PMU) and Merging Units (MU), rely on Global Navigation Satellite Signals (GNSS), often GPS, for time transfer. Recently, sophisticated ‘spoofing’ attacks have become feasible due to the availability of low cost Software Defined Radio (SDR) systems. The proposed method uses multiple GNSS receive antennas placed in close proximity at the electricity substation, such that it is not possible for an attacker to target a unique spoofing signal towards each antenna. In a system employing three or more receive antennas, during a spoofing attack two or more of the GNSS receive antennas will return an estimated position in impossible locations. This is sufficient to raise alarm that time sensitive applications should use an alternative time source or holdover clock. The contributions of this paper include a detailed description of the proposed method, an experimental assessment of GNSS receiver and substation clock position estimation variance to establish the minimum separation required between receive antennas, and a validation of the method by experimental demonstration. A further benefit of the authors’ method is that it may be put into practice immediately using commercial-off-the-shelf (COTS) substation clock equipment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".