Demonstration of a Multi-Layer Spoofing Detection Implemented in a High Precision GNSS Receiver
Bibliographic record
Abstract
Civilian applications including vehicular navigation, electrical power grids and digital communication networks are relying on GNSS-based position and timing services and motivation has increased to disrupt these systems and endanger safety of life and critical applications. GNSS signals are susceptible to jamming and spoofing attacks due to being weak near the earth's surface. Herein, realistic spoofing scenarios and their features will be characterized. This characterization is based on spoofing/authentic relative signal power, how synchronous the spoofing signals are to the authentic ones and the availability of both spoofing and authentic signals. A variety of detection methods are defined, using metrics derived at different layers of a GNSS receiver with a single antenna input. An on-board spoofing detection unit was implemented on NovAtel's OEM7 family of receivers. This unit collects different metrics from the GNSS signal processing chain and provides a real-time indication if the receiver is under spoofing attack. The probability of false detection during jamming or multipath conditions is given special consideration, since a false declaration of the presence of a spoofing attack could lead to incorrect, unnecessary or even dangerous reactions from the user or spoofing mitigation implementations. However, if spoofing signals are present, the user (and the receiver) must be aware of these to choose an appropriate course of action. Test results are presented using several spoofing scenarios based on GNSS hardware simulator, repeaters and software defined radios in conditions ranging from stationary to kinematic, with low and high levels of multipath.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".