# A Survey on Satellite Communication System Security
## 7 Future Research Directions
### AI-Based Satellite Communication Security  
As attacks on SCSs become more advanced, traditional security methods, such as rule- or behavior-based methods, are no longer adequate for countering them. Identifying the attack patterns has become a challenge, and in response, security methods based on AI have been developed, since AI-powered systems are capable of finding more sophisticated patterns that are difficult for humans to detect. Therefore, it is thought that AI will play a significant role in the next generation of SCS security methods [145; 146].  
In signal management, ML-powered methods that distinguish benign signals from malicious signals in SCSs have been proposed. For example, ref. [147] introduces a novel ML methodology designed to automatically detect both short-term and long-term interferences within satellite communication spectra in real time. Similarly, ref. [148] proposes a deep-learning-based system for managing interference, a significant issue in both terrestrial and satellite networks. These ML techniques enhance the accuracy of detecting carrier signals in the frequency domain, which is vital for securing communication channels against unauthorized access and interference. On the other hand, a few studies [149; 150; 151] have suggested mitigating anti-jamming using AI based on signal management. These studies focused on enhancing real-time interference detection using ML to block jamming of SCSs.  
Additionally, optimizing ISL communications using deep reinforcement learning methods makes SCSs more robust and secure [145; 152]. These methods help in optimizing routing and satellite selection, thereby enhancing the overall security posture by ensuring stable and secure satellite connections and mitigating eavesdropping attacks.  
Lastly, AI techniques can be leveraged to forecast and manage network traffic in satellite communications, which includes preventing potential cyber attacks and ensuring the integrity of data transmission. Neural networks and other AI models aid in proactive security management, adapting dynamically to new threats. For example, AI-based network intrusion detection technologies used in terrestrial networks could be applied. If the existing models are adapted and made more lightweight for satellite communication systems, it could be possible to defend against various attacks [153]. Recently, federated learning (FL) has emerged as a promising direction for security. FL, as a new method that protects user privacy and data privacy, could potentially replace traditional ML and DL methods. Utilizing FL could enhance the overall network availability of satellite communication networks through traffic classification [154; 155].  
Leveraging AI technologies has enhanced SCS's security in various fields, such as signal management, ISL communication optimization, and traffic management. However, AI can be utilized for more sophisticated attacks on SCSs. For instance, in satellite communication systems, attacks could involve the application of encrypted traffic classification methods, e.g., ET-BERT [156], to execute fingerprinting attacks on satellite communication traffic. Satellite communication systems broadcast signals via RF, which makes it easy to randomly collect traffic data and build datasets, which could facilitate various attacks on these systems [157; 158; 159].  
In addition to the issues mentioned above, AI-powered SCSs also have limitations. For example, AI in SCSs brings limitations such as a limited access to and a reduced quality of data, which can be noisy and incomplete, making it challenging to develop and train accurate models. Additionally, the computational resources available on satellites are often constrained [160], which can limit the efficiency and feasibility of advanced AI algorithms. The reliability and robustness of AI systems must also be maintained in extreme operational environments; however, AI models may falter when faced with new situations or anomalies not present in the training data [14].