Synthetic GPS Data Generation and AI Detection Response for Spoofing on Maritime Autonomous Surface Ships
Bibliographic record
Abstract
GPS spoofing poses a critical threat to maritime autonomous surface ships, compromising navigation integrity and situational awareness. Existing research is limited by the lack of realistic datasets and reproducible evaluation environments. In this work, we present a comprehensive framework for GPS spoofing detection, combining a modular simulation of spoofing attacks(ghost vessel, gradual drift, location jumps, replay and meaconing), machine learning-based detection, and an automated response module. Our simulator generates over 950 labeled spoofed AIS points merged with normal data to create a ground-truth dataset suitable for model training and evaluation. Among the evaluated models, a GRU-based approach achieved the best performance, with an F1-score of 0.98, high recall, and only six false negatives. The integrated response module applies debouncing logic to classify suspicious events and triggers email alerts for confirmed spoofing, enabling real-time operational monitoring. These results demonstrate that our framework provides a scalable and effective reproducible solution for enhancing maritime navigation security.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".