Secure Satellite Communications Using Machine Learning

Satellite communication is an important technology for providing communication services over large geographical areas and for transmitting sensitive military, navigational, commercial, and other critical data. With the growth of 6G, IoT, and Low Earth Orbit (LEO) satellite constellations, secure satellite communication has become increasingly important. However, wireless and broadcast characteristics expose satellite networks to eavesdropping, spoofing, jamming, signal interference, and intrusion attacks. This paper presents an AI-driven security framework based on Machine Learning (ML), Deep Learning, Reinforcement Learning (RL), and Federated Learning. One-Class SVM and Sparse Autoencoder are considered for physical-layer authentication; LSTM/RNN models are used for telemetry anomaly and intrusion detection; CNN/CNN-LSTM models are used for jamming detection; RL is used for adaptive beamforming and artificial-noise power allocation; and Federated Learning supports privacy-preserving collaborative training. The proposed architecture integrates these modules to provide adaptive satellite-security capabilities. The stated performance values are project targets and require experimental validation.