Репозиторий Евразийского национального университета имени Л.Н. Гумилева
Репозиторий Евразийского национального университета имени Л.Н. Гумилева
Репозиторий Евразийского национального университета имени Л.Н. Гумилева
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Advanced Cybersecurity Framework for LEO Aerospace: Integrating Quantum Cryptography, Artificial Intelligence Anomaly Detection, and Blockchain Technology

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Автор
Bakyt, Makhabbat
Spada, Luigi La
Zeeshan, Nida
Moldamurat, Khuralay
Atanov, Sabyrzhan
Konyrkhanova, Assem
Yurkov, Nikolay
Kuanysh, Absalyam
Marat, Yertis
Tilenbayev, Alzhan
Дата
2025
Редактор
Journal of Robotics and Control
ISSN
2715-5072
Аннотации
This study aims to enhance the security of highspeed 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.
URI
http://repository.enu.kz/handle/enu/29988
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