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

Makhabbat Bakyt; Luigi La Spada; Nida Zeeshan; Khuralay Moldamurat; Sabyrzhan Atanov; Assem Konyrkhanova; Nikolay Yurkov; Absalyam Kuanysh; Yertis Marat; Alzhan Tilenbayev

doi:10.18196/jrc.v6i2.25918

Authors

Makhabbat Bakyt L.N. Gumilyov Eurasian National University
Luigi La Spada Edinburgh Napier University
Nida Zeeshan Edinburgh Napier University
Khuralay Moldamurat L.N. Gumilyov Eurasian National University
Sabyrzhan Atanov L.N. Gumilyov Eurasian National University
Assem Konyrkhanova L.N. Gumilyov Eurasian National University
Nikolay Yurkov Penza State University
Absalyam Kuanysh L.N. Gumilyov Eurasian National University
Yertis Marat National Defense University
Alzhan Tilenbayev L.N. Gumilyov Eurasian National University

DOI:

https://doi.org/10.18196/jrc.v6i2.25918

Keywords:

Quantum Encryption, Artificial Intelligence Anomaly Detection, Blockchain, Aerospace Security, Low Earth Orbit (LEO)

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.

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