Physical Layer Authentication for Satellite Communication Systems Using Machine Learning
Bibliographic record
Abstract
The vertical heterogeneous network (VHetNets) architecture aims to provide global connectivity for a variety of services by combining terrestrial, aerial, and space networks. Satellites complement cellular networks to overcome coverage and reliability limitations. However, the services of low-earth orbit (LEO) satellites are vulnerable to spoofing attacks. Physical layer authentication (PLA) can provide robust satellite authentication using machine learning (ML) with physical attributes. In this paper, an adaptive PLA scheme is proposed using Doppler frequency shift (DS) and received power (RP) features with a one-class classification support vector machine (OCC-SVM). One class-classification is a ML technique for outlier and anomaly detection which uses only legitimate satellite training data. This scheme is evaluated for fixed satellite services (FSS) and mobile satellite services (MSS) at different altitudes. Results are presented which show that the proposed scheme provides a higher authentication rate (AR) using DS and RP features simultaneously compared to other approaches in the literature.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".