Quantum-Resilient Federated Learning for Multi-Layer Cyber Anomaly Detection in UAV Systems

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dc.contributor.authorSahin, Canan Batur
dc.date.accessioned2026-06-19T06:37:40Z
dc.date.available2026-06-19T06:37:40Z
dc.date.issued2026
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractUnmanned Aerial Vehicles (UAVs) are increasingly used in civilian and military applications, making their communication and control systems targets for cyber attacks. The emerging threat of quantum computing amplifies these risks. Quantum computers could break the classical cryptographic schemes used in current UAV networks. This situation underscores the need for quantum-resilient, privacy-preserving security frameworks. This paper proposes a quantum-resilient federated learning framework for multi-layer cyber anomaly detection in UAV systems. The framework combines a hybrid deep learning architecture. A Variational Autoencoder (VAE) performs unsupervised anomaly detection. A neural network classifier enables multi-class attack categorization. To protect sensitive UAV data, model training is conducted using federated learning with differential privacy. Robustness against malicious participants is ensured through Byzantine-robust aggregation. Additionally, CRYSTALS-Dilithium post-quantum digital signatures are employed to authenticate model updates and provide long-term cryptographic security. Researchers evaluated the proposed framework on a real UAV attack dataset containing GPS spoofing, GPS jamming, denial-of-service, and simulated attack scenarios. Experimental results show the system achieves 98.67% detection accuracy with only 6.8% computational overhead compared to classical cryptographic approaches, while maintaining high robustness under Byzantine attacks. The main contributions of this study are: (1) a hybrid VAE-classifier architecture enabling both zero-day anomaly detection and precise attack classification, (2) the integration of Byzantine-robust and privacy-preserving federated learning for UAV security, and (3) a practical post-quantum security design validated on real UAV communication data.
dc.identifier.doi10.3390/s26020509
dc.identifier.issn1424-8220
dc.identifier.issue2
dc.identifier.pmid41600307
dc.identifier.scopus2-s2.0-105028707327
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/s26020509
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5154
dc.identifier.volume26
dc.identifier.wosWOS:001671388400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorSahin, Canan Batur
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSensors
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectUav Security
dc.subjectFederated Learning
dc.subjectPost-Quantum Cryptography
dc.subjectAnomaly Detection
dc.subjectCrystals-Dilithium
dc.subjectDifferential Privacy
dc.subjectVariational Autoencoder
dc.titleQuantum-Resilient Federated Learning for Multi-Layer Cyber Anomaly Detection in UAV Systems
dc.typeArticle

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