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.
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".