## 4 Experimental Results and Discussion
### Discussion and Future Work
Traditional security models often address isolated vulnerabilities or specific attack vectors, which are less effective in dynamic environments. By incorporating multisegment interactions and quantifying countermeasure impacts, TV-HARM offers a more comprehensive framework for security assessments, making it highly applicable to real-world satellite network defense strategies. To further enhance TV-HARM's applicability, considerations such as bandwidth limitations, onboard computational constraints, and real-time security demands should be addressed. Refining the model to support lightweight threat detection mechanisms will improve its adaptability for resource-constrained satellite platforms. Future research should explore the tradeoff between security improvements and system performance, considering limitations in satellite payloads, real-time decision making, and ground station connectivity for updates. Additionally, large-scale simulations and real-world case studies will offer valuable insights into the model's performance, reinforcing its potential as a scalable, adaptable security framework for next-generation satellite networks. Building upon these findings, several challenges remain for future work. First, this study primarily focused on software and network vulnerabilities, while wireless-communication-specific threats, such as jamming and spoofing, were beyond the scope of this research. Future research should address radio-frequency (RF) attacks and signal manipulation techniques to achieve a more comprehensive evaluation of satellite network security. The implementation of anti-jamming strategies, GNSS-spoofing detection methods, and interference mitigation techniques will strengthen the security framework of satellite communications. Second, the application of AI-based security mechanisms for real-time threat detection and mitigation requires further exploration. One potential direction is the use of large language models (LLMs) for packet-level anomaly detection in satellite networks. Future studies should investigate how LLM-based models can analyze telemetry data and communication logs to detect malicious patterns in network traffic. By training LLMs on extensive datasets of legitimate and anomalous satellite communications, these models could improve adaptive threat detection and reduce response times against emerging cyberthreats. Additionally, combining deep-learning techniques with LLM-based models may enhance protocol behavior analysis, enabling the automated identification of unauthorized command injections and spoofed control messages. Finally, although the mitigation strategies evaluated in this study proved to be effective in a controlled test environment, their practical implementation in operational satellite networks presents additional challenges. The remote deployment of security patches is particularly challenging in space environments because of strict update validation requirements, limited downlink/uplink bandwidths, and the risk of service disruptions. Furthermore, protocol patching often requires firmware modifications, which may not be feasible for legacy satellite systems. Future research should investigate secure, lightweight update mechanisms that minimize operational overhead while ensuring the timely deployment of security patches.