# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 4 Review
### Beamforming
This makes it challenging for [PERSON] to eavesdrop, as she would need to be positioned within the narrow beam path and possess sophisticated equipment to capture and analyze the signal. Static beamforming, where the direction remains fixed, is susceptible to attackers who can learn the beam pattern and position themselves to intercept signals [80]. Dynamic beamforming, where the direction changes frequently, offers better security but requires more complex implementation. Even with dynamic beamforming, there is a possibility of misalignment due to imperfect channel knowledge or rapid user movement. This can create unintended signal leakage and weaken security [81; 82]. In [83], convolutional neural network-based beamforming is proposed to handle complex data in a multi-user multi-input single-output (MISO) system. For the optimization of the SNR, a novel unsupervised learning approach is utilized. In [84], system security and spectral efficiency are improved using intelligent reflecting surfaces. Assuming imperfect CSI, the system weighted achievable sum rate was maximized, subject to various constraints, such as the minimum achievable rate constraint of each user and the maximum transmit power of the satellite and base station. In [85], the authors explore secure communication in a cognitive satellite-terrestrial network with a software-defined architecture. They propose beamforming schemes that use terrestrial network interference to enhance satellite network security, leveraging shared millimeter-wave frequencies. The goal is to minimize the total transmit power while meeting quality-of-service and secrecy rate requirements. Two efficient BF schemes are introduced: one for a single eavesdropper using a second-order cone transformation and penalty function, and another for multiple eavesdroppers using a two-layer iterative scheme. In [86], the authors studied the PLS of a hybrid satellite-terrestrial relay network with multiple eavesdroppers. The secrecy performance using amplify-and-forward and decode-and-forward protocols for both non-colluding and colluding eavesdroppers was analyzed. The study derived secrecy outage probability (SOP) expressions for different fading models, provided user-relay selection criteria, and validated the analysis through simulations, highlighting the impact of various parameters on secrecy performance. In [87], the minimization of power consumed by a multibeam satellite was achieved with a constraint on the secrecy outage probability by proposing joint beamforming and power allocation via alternating optimization to find better solutions iteratively. In [88], the authors explore 6G wireless networks, emphasizing global high-speed connectivity, especially in under-connected areas. ISTNs are proposed to address challenges like long delays, large-scale device coverage, and real-time services. To tackle these, the authors introduce an optimal multibeam design using machine learning, focusing on user clustering, energy-efficient resource allocation, and distributed computing. Case studies cover disaster relief and satellite-terrestrial IoT networks. Future research directions include digital twins and quantum-inspired optimization to enhance multibeam design in 6G ISTNs. In [89], the authors address resource allocation in ISTNs, focusing on improving spectrum efficiency and fairness by using beam-hopping technology. The study examines how satellite and terrestrial systems can share the same frequency band, creating dynamic protection zones to avoid interference. Two resource allocation problems are formulated: maximizing the weighted sum of capacity and maximizing the minimum capacity-to-demand ratio. The problems are solved using mixed-integer linear program