Advanced Cybersecurity Framework for LEO Aerospace: Integrating Quantum Cryptography, Artificial Intelligence Anomaly Detection, and Blockchain Technology

This study aims to enhance the security of high-speed Low Earth Orbit (LEO) communication systems by developing an integrated, multi-layered security framework that addresses the limitations of current aerospace cybersecurity measures. The primary challenge lies in ensuring real-time data confidentiality, integrity, and authenticity in the face of sophisticated quantum and spoofing threats. To overcome these issues, the research contribution is the design and evaluation of a unified framework that combines quantum-resistant encryption using a FACT system with a Quantis USB quantum random number generator, an LSTM encoder-decoder model for real-time anomaly detection in ADS-B messages, and a blockchain-based mechanism for immutable data logging. The methodology involves benchmarking quantum-enhanced AES against traditional encryption schemes, training the LSTM network to detect subtle anomalies in flight data, and assessing blockchain scalability under high transaction loads. Results indicate significant improvements in encryption speed and detection accuracy—demonstrating up to a 30% increase in anomaly detection performance—while also revealing challenges such as increased computational overhead and scalability limitations in blockchain implementation. The framework shows promise for practical applications in satellite communications and air traffic management, though further research is needed to optimize resource consumption and enhance system resilience under extreme operational conditions.

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Publication Details

Journal
Journal of Robotics and Control (JRC)
Published
2025-03-24
DOI
https://doi.org/10.18196/jrc.v6i2.25918
Citations
5
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
16.23
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article

Advanced Cybersecurity Framework for LEO Aerospace: Integrating Quantum Cryptography, Artificial Intelligence Anomaly Detection, and Blockchain Technology

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5 citations
Journal of Robotics and Control (JRC)
Blockchain Technology Applications and Security
16.23
article

Advanced Cybersecurity Framework for LEO Aerospace: Integrating Quantum Cryptography, Artificial Intelligence Anomaly Detection, and Blockchain Technology

N. К. Yurkov, Nida Zeeshan, Luigi La Spada, Sabyrzhan Atanov, Khuralay Moldamurat, Assem Konyrkhanova, Makhabbat Bakyt, Alzhan Tilenbayev, Absalyam Kuanysh, Yertis Marat
article en
5 citations

Abstract

This study aims to enhance the security of high-speed Low Earth Orbit (LEO) communication systems by developing an integrated, multi-layered security framework that addresses the limitations of current aerospace cybersecurity measures. The primary challenge lies in ensuring real-time data confidentiality, integrity, and authenticity in the face of sophisticated quantum and spoofing threats. To overcome these issues, the research contribution is the design and evaluation of a unified framework that combines quantum-resistant encryption using a FACT system with a Quantis USB quantum random number generator, an LSTM encoder-decoder model for real-time anomaly detection in ADS-B messages, and a blockchain-based mechanism for immutable data logging. The methodology involves benchmarking quantum-enhanced AES against traditional encryption schemes, training the LSTM network to detect subtle anomalies in flight data, and assessing blockchain scalability under high transaction loads. Results indicate significant improvements in encryption speed and detection accuracy—demonstrating up to a 30% increase in anomaly detection performance—while also revealing challenges such as increased computational overhead and scalability limitations in blockchain implementation. The framework shows promise for practical applications in satellite communications and air traffic management, though further research is needed to optimize resource consumption and enhance system resilience under extreme operational conditions.

Journal of Robotics and Control (JRC)Vol. 6(2)
L. N. Gumilyov Eurasian National University (KZ), Penza State University (RU), Edinburgh Napier University (GB)
Openalex Percentile: Top 1%
Blockchain Technology Applications and Security
16.23
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