Startseite Security challenges from physical to network layers in satellite free-space optical communication
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Security challenges from physical to network layers in satellite free-space optical communication

  • Indu Bala und Vivek Arya ORCID logo EMAIL logo
Veröffentlicht/Copyright: 16. September 2025
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Abstract

Satellite-based free-space optical (FSO) communication has emerged as a transformative paradigm, offering spectrum-efficient connectivity with ultra-high bandwidth to support bandwidth-intensive applications and enable global coverage. As compared to the conventional RF systems, FSO provides distinct advantages such as high data rates, interference immunity, and license-free operation. Despite its unique characteristics like narrow optical beams, dynamic orbital environments, and increasing architectural complexity, there exist diverse security vulnerabilities across multiple protocol layers. In this paper, a comprehensive review on the security issues in satellite FSO systems, spanning the physical, data link, and network layers is provided. Critical threats and vulnerabilities at each layer are analyzed, followed by a discussion of state-of-the-art countermeasures, including physical-layer security mechanisms, adaptive beam control, advanced cryptographic schemes, quantum key distribution, and AI-driven anomaly detection. Key challenges, such as scalability, trust management, atmospheric impairments, and post-quantum security, are also highlighted as pressing concerns for future FSO networks. Furthermore, the chapter offers insights and design recommendations for developing intelligent, secure, and resilient satellite-based FSO communication infrastructures capable of addressing evolving cyber security threats while maintaining high performance.


Corresponding author: Vivek Arya, Department of ECE, UIE, Chandigarh University Mohali-140413, Sahibzada Ajit Singh Nagar, Punjab, India, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2025-08-18
Accepted: 2025-08-31
Published Online: 2025-09-16

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 27.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/joc-2025-0350/html
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