Detection and Mitigation of GNSS Spoofing Attacks in Maritime Environments Using a Genetic Algorithm
Bibliographic record
Abstract
Due to the high reliance of daily activities on the Global Navigation Satellite System (GNSS), its security is one of the major concerns for research and industry. Most navigation and mobile-driven location-based services use GNSS to render services. Due to the low power and easy access of GNSS signals, these signals are vulnerable to spoofing and other types of attacks. Recently many GNSS spoofing attacks have been identified in road- and maritime-based environments. This study provides a technique to detect and counter the GNSS spoofing attack in the maritime environment. This technique uses the Receiver Autonomous Integrity Monitoring (RAIM) model with Least Square Estimation (LSE) and Proportional Integral Derivative (PID) Control to detect the spoofing attack. The proposed technique is based on the concept of a genetic algorithm and navigation devices, such as inertial sensors and pilot options for the ship. A case study using the AIS dataset and simulation using MATLAB and NS3 is provided to validate the performance of the proposed approach. Nine different voyages from the AIS dataset were considered to check the accuracy and performance of the proposed algorithm. The accuracy of the proposed technique was analyzed using the correctly identified attack. The result shows that the proposed technique identifies spoofing attacks with an average value of 90 percent. For result analysis the considered nine routes were traversed multiple times. Root mean square error is used to calculate the positional mismatch (error rate). Based on the combined results analysis, the average value of RMSE is 0.28. In a best-case scenario, the proposed approach provides an RMSE value of 0.009.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".