Authentication for Satellite Communication Systems Using Physical Characteristics
Bibliographic record
Abstract
Satellite communication networks have gained a lot of attention recently as a solution to mitigate the limitations of terrestrial networks such as stability and coverage. However, integrating satellite and terrestrial networks makes the system more vulnerable to spoofing attacks. Thus, robust and effective authentication is required. Physical layer authentication (PLA) has emerged as an alternative paradigm that uses physical characteristics to achieve authentication. In this paper, PLA is proposed for low earth orbit (LEO) satellites using the Doppler frequency shift (DS) and received power (RP) characteristics. Hypothesis testing using a threshold or machine learning (ML) is considered to discriminate between legitimate and illegitimate satellites. For ML, a one-class classification support vector machine (OCC-SVM) is employed which uses training data from only legitimate users. The performance is evaluated using real satellite data from the system tool kit (STK). Results are presented which show that the authentication rate (AR) with DS is higher than with RP at low elevation angles for both schemes, but is higher with RP at high elevation angles. Further, the ML authentication scheme provides a higher AR than the threshold scheme for a small percentage of the training data considered as outliers, but at larger percentages the OR threshold scheme is better.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".