# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 4 Review
### Physical Characteristics
By exploiting atmospheric effects like thermal noise and tropospheric scintillation fading, the architecture detects rapid signal fluctuations at ground stations. Signals from legitimate satellites and spoofing APs exhibit distinct noise patterns due to their differing spatial paths through dynamic atmospheric conditions. Wavelet filtering is utilized to extract these variations, processing them through a Long Short-Term Memory network. This approach highlights the efficacy of leveraging physical layer attributes to improve cybersecurity in evolving communication networks. In [104], the authors propose a novel signal processing algorithm to enhance the PLS of an ISTN against spoofing attacks. The data were collected from two GEO satellites: Nilesat 7\({}^{\circ}\) W and Eutelsat 33\({}^{\circ}\) E. The attack model assumes Nilesat to be the legitimate satellite and Eutelsat to be the adversary. The \(E_{S}/N_{0}\) (dB) of the adversary is varied by adding a term \(\delta\) so as to mimic the legitimate satellite. From the time series \(E_{S}/N_{0}\) (dB) of the satellites, the rapidly varying signal component (RVSC) is extracted. The algorithm utilizes atmospheric signatures that cause rapid signal variations at ground stations, leveraging the spatial time-frequency characteristics associated with incoming signals from different satellites to distinguish their sources, such as the mean frequency, median frequency, bandwidth, standard deviation, kurtosis, and skewness [109]. The parameters derived from these signal variations are fed into a machine learning algorithm, such as a neural network, support vector machine, or Ensemble method, to authenticate legitimate satellites. The details of the ML models can be found in [104]. The results are shown in Figures 6-8. These results show the MDR, FAR, and AR as the adversary tunes its \(E_{S}/N_{0}\) (dB) by varying \(\delta\). Also, the results obtained with direct samples of \(E_{S}/N_{0}\) (dB) are shown for comparison. As the results show, the performance is consistent with the physical properties obtained with the RVSC of the satellite's signal. The results demonstrate an authentication rate exceeding 97%, with low missed detection and false alarm rates. This method offers the potential for more secure ISTNs. Future research may explore the method's performance with closely positioned GEO satellites, where increased channel correlation could impact performance. Figure 6: The MDR as a function of the adversary satellite’s \(\delta\) (dB) relative to the legitimate satellite. The adversary attempts to spoof the legitimate satellite by adjusting its \(E_{S}/N_{0}\) (dB). The results demonstrate that the proposed RVSC method consistently maintains a low MDR, even under increasingly challenging spoofing conditions. In contrast, the direct detection method exhibits a significantly higher MDR with NN, indicating its vulnerability to spoofing attacks. Reprinted with permission from ref. [104]. Copyright: 21 October 2024 IEEE. Figure 7: The FAR as a function of the adversary satellite’s \(\delta\) (dB) relative to the legitimate satellite. The results demonstrate that the proposed RVSC method consistently maintains a low FAR, even under increasingly challenging spoofing conditions. In contrast, the direct detection method exhibits a significantly higher FAR, indicating its vulnerability to spoofing attacks. Reprinted with permission from ref. [104]. Copyright: 21 October 2024 IEEE. In Table 5, we provide summary of works done using physical characteristics to improve the PLS of ISTNs. In the context of ISTNs, modeling and estimating channel characteristics pose greater challenges. Accurately modeling the complex and dynamic nature of ISTN channels is crucial for effective PLS implementation. The complex interplay between terrestrial and satellite links, coupled with diverse propagation environments, introduces additional complexities. Factors such as satellite mobility, atmospheric conditions, and shadowing effects further complicate channel modeling.