# Low Earth Orbit Satellite Security and Reliability: Issues, Solutions, and the Road Ahead

arXiv: https://arxiv.org/abs/2201.03063 · HTML: https://mlsanity.com/paper/2201.03063/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2022.

## Built on by (most cited first, 4 of 4)

- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networks](https://mlsanity.com/paper/2512.08882/) (2025)
- [Reinforcement Learning-enabled Satellite Constellation Reconfiguration and Retasking for Mission-Critical Applications](https://mlsanity.com/paper/2409.02270/) (2024)
- [Temporal and Multimodal Deep Learning for Cyberattack Detection in LEO Satellite Systems](https://mlsanity.com/paper/2609.10746/) (2026)

## Abstract (arXiv)

Low Earth Orbit (LEO) satellites undergo a period of rapid development driven by ever-increasing user demands, reduced costs, and technological progress. Since there is a paucity of literature on the security and reliability issues of LEO Satellite Communication Systems (SCSs), we aim to fill this knowledge gap. Specifically, we critically appraise the inherent characteristics of LEO SCSs and elaborate on their security and reliability requirements. In light of this, we further discuss their vulnerabilities, including potential security attacks launched against them and reliability risks, followed by outlining the associated lessons learned. Subsequently, we discuss the corresponding security and reliability enhancement solutions, unveil a range of trade-offs, and summarize the lessons gleaned. Furthermore, we shed light on several promising future research directions for enhancing the security and reliability of LEO SCSs, such as integrated sensing and communication, computer vision aided communications, as well as challenges brought about by mega-constellation and commercialization. Finally, we summarize the lessons inferred and crystallize the take-away messages in our design guidelines.