PAST-AI: Physical-layer Authentication of Satellite Transmitters via Deep Learning
Physical-layer security is regaining traction in the research community, due to the performance boost introduced by deep learning classification algorithms. This is particularly true for sender authentication in wireless communications via radio fingerprinting. However, previous research efforts mai...
Gespeichert in:
Hauptverfasser: | , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Physical-layer security is regaining traction in the research community, due
to the performance boost introduced by deep learning classification algorithms.
This is particularly true for sender authentication in wireless communications
via radio fingerprinting. However, previous research efforts mainly focused on
terrestrial wireless devices while, to the best of our knowledge, none of the
previous work took into consideration satellite transmitters. The satellite
scenario is generally challenging because, among others, satellite radio
transducers feature non-standard electronics (usually aged and specifically
designed for harsh conditions). Moreover, the fingerprinting task is
specifically difficult for Low-Earth Orbit (LEO) satellites (like the ones we
focus in this paper) since they orbit at about 800Km from the Earth, at a speed
of around 25,000Km/h, thus making the receiver experiencing a down-link with
unique attenuation and fading characteristics. In this paper, we propose
PAST-AI, a methodology tailored to authenticate LEO satellites through
fingerprinting of their IQ samples, using advanced AI solutions. Our
methodology is tested on real data -- more than 100M I/Q samples -- collected
from an extensive measurements campaign on the IRIDIUM LEO satellites
constellation, lasting 589 hours. Results are striking: we prove that
Convolutional Neural Networks (CNN) and autoencoders (if properly calibrated)
can be successfully adopted to authenticate the satellite transducers, with an
accuracy spanning between 0.8 and 1, depending on prior assumptions. The
proposed methodology, the achieved results, and the provided insights, other
than being interesting on their own, when associated to the dataset that we
made publicly available, will also pave the way for future research in the
area. |
---|---|
DOI: | 10.48550/arxiv.2010.05470 |