Prof. Shlomi Arnon

The integration of satellite and terrestrial networks would generate more opportunities for various kinds of cyber attacks, such as spoofing. In this study, we propose a novel signal-processing algorithm for improving the physical layer security of integrated satellite-terrestrial networks (ISTN). The proposed algorithm is based on utilizing the atmospheric signature associated with the received signal at the ground station that causes rapid variations in the signal. We leverage the characteristics of spatial time-frequency signature caused by atmospheric channel associated with incoming signals at the ground station from different satellites to differentiate among the sources from which the signal has been transmitted. The various parameters extracted from the rapidly varying signal component are then provided as inputs to the machine learning algorithm for detecting the legitimate satellite. The algorithm is validated against the measured data obtained at a site located in Israel. The results shows very low false alarm (< 2%) and missed detection rate (< 1% ) while the authentication rate is quite high (> 97%).