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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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