Robust and Resilient Satellite Communication

This paper presents a modular framework for secure and resilient satellite communication in Low Earth Orbit (LEO), integrating post-quantum cryptography (PQC), anomaly detection, and trust-aware federated learning (FL). We implement a hybrid cryptographic stack combining Hamming Quasi-Cyclic (HQC) and dual-mode Advanced Encryption Standard (AES) for both confidentiality and side-channel resilience. To detect cyber-physical anomalies, we construct a synthetic telemetry dataset and evaluate lightweight models, finding that a combined convolutional neural network (CNN) and gated recurrent unit (GRU) architecture (CNN-GRU) achieves the best trade-off between accuracy and minority-class recall. We then deploy the anomaly detector in a federated learning setting and propose a trust-based aggregation method that gradually downweights poisoned updates. Compared to static defences, our approach maintains high accuracy even when malicious clients are in the majority. These results highlight the importance of a multi-faceted security strategy grounded in cryptography-machine learning (ML) integration for autonomous and trustworthy space systems.