Responsible Deep-Federated-Learning-Based Threat Detection for Satellite Communications

Sara Salim, Nour Moustafa, Abdulrazaq Almorjan

Abstract: Satellite communications (Satcoms) have become an indispensable part of modern society, enabling various applications ranging from global connectivity to critical disaster management. However, reliance on Satcoms also raises concerns about cyber threats, highlighting the need for robust detection mechanisms. This article presents a novel model for threat detection (TD) in Satcoms, leveraging deep-federated learning (DFL). Our DFL-based TD model utilizes variational autoencoders (VAEs) as local models, strategically deployed across satellite ground stations and communication nodes. This architecture is meticulously tailored to the distributed nature of Satcom systems, ensuring robust data privacy at local nodes while enhancing collective TD capabilities. To protect sensitive information during model updates, we integrate differential privacy and secure aggregation techniques, reinforcing the model’s commitment to data confidentiality. Furthermore, we prioritize trustworthiness and accountability in TD processes by emphasizing responsible AI practices, explainability, and ethical compliance. By incorporating SHAP values and local explanation methods, we significantly enhance transparency in decision making, empowering stakeholders to effectively understand and interpret model outputs. In addition, our model addresses bias mitigation and fairness concerns, striving for equitable treatment across diverse data subsets and promoting inclusivity. Real-world case studies demonstrate the model’s effectiveness in detecting anomalies in Satcoms, supporting predictive maintenance, and enhancing network security.

External IDs:dblp:journals/iotj/SalimMA25