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Secure Satellite Communications Using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: 10.64388/IREV10I4-1724406
Abstract
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.
Keywords
Satellite security; Machine Learning; Deep Learning; LEO; jamming detection; intrusion detection; physical-layer authentication; reinforcement learning; federated learning
References
[1] Zhou F, Zhu X. Reinforcement Learning for Secure Semantic LEO Satellite Networks: Joint Fidelity-Secrecy Power Allocation. Sensors. 2026;26(8):2546.
[2] Abdrabou M, Gulliver TA. Physical Layer Authentication for Satellite Communication Systems using Machine Learning. IEEE Open Journal of the Communications Society. 2022.
[3] Li K, Zhou H, Tu Z, Wang W, Zhang H. Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning. IEEE Access. 2020.
[4] Zhu C, et al. Jamming Recognition Based on Feature Fusion and Convolutional Neural Network. Journal of Beijing Institute of Technology. 2022.
[5] AI-driven secure satellite communication system using machine learning for intrusion detection, anti-jamming and adaptive authentication [project synopsis]. Unpublished.
How to cite this paper
@article{1724406,
author = {Ankur Pal, Brijendra Gupta},
title = {Secure Satellite Communications Using Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {4363-4368},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724406.pdf},
abstract = {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.},
keywords = {Satellite security; Machine Learning; Deep Learning; LEO; jamming detection; intrusion detection; physical-layer authentication; reinforcement learning; federated learning},
month = {October},
doi = {https://doi.org/10.64388/IREV10I4-1724406}
}