GPS Vulnerability to Spoofing Threats and a Review of Antispoofing Techniques
Bibliographic record
Abstract
GPS-dependent positioning, navigation, and timing synchronization procedures have a significant impact on everyday life. Therefore, such a widely used system increasingly becomes an attractive target for illicit exploitation by terrorists and hackers for various motives. As such, spoofing and antispoofing algorithms have become an important research topic within the GPS discipline. This paper will provide a review of recent research in the field of GPS spoofing/anti-spoofing. The vulnerability of GPS to a spoofing attack will be investigated and then different spoofing generation techniques will be discussed. After introducing spoofing signal model, a brief review of recently proposed anti-spoofing techniques and their performance in terms of spoofing detection and spoofing mitigation will be provided. Limitations of anti-spoofing algorithms will be discussed and some methods will be introduced to ameliorate these limitations. In addition, testing the spoofing/anti-spoofing methods is a challenging topic that encounters some limitations due to stringent emission regulations. This paper will also provide a review of different test scenarios that have been adopted for testing anti-spoofing techniques.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".