An approach to detect GNSS spoofing
Bibliographic record
Abstract
The focus here has been on correct detection of spoofing attacks from interference sources. To this end several predespreading and postdespreading spoofing detection metrics, namely temporal/ spectral analyses, SPCA, C/N0, and SQM, were implemented and analysed under different interference signals, namely CW jammer, wideband noise, chirp jammer, and multipath. Considering the real data analysis results, the predespreading detection metrics, namely variance analysis and SPCA, are not affected under multipath and hence used to discriminate between spoofing and multipath signals based on the assumption that these metrics are not affected in typical multipath scenarios. The assumption was validated by collecting several data sets in dense urban environments and analysing the metric results. The temporal/spectral analyses in the presence of jamming signals were affected. Among jamming signals, the chirp jammer had the most destructive effect on the performance of a receiver and consequently severely affected the performance of the postdespreading detection metrics. The chirp jammer also affected the SPCA spoofing detection metric and its behaviour on detection metrics is very similar to that of a nonoverlapped spoofing attack. The SQM metric was implemented to detect spoofing and multipath at the postdespreading level. As shown in the scenarios used, the SQM metric is not overly sensitive for short-range multipath/spoofing signals.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".