S-LARF: Layered Adversarial Resilience Framework for spaceborne anomaly detection

Fadwa Belali, Abdellah Essetty and Slimane Bah

International Journal of Critical Infrastructure Protection, 2026, vol. 53, issue C

Abstract: The rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) in CubeSat missions enables increased onboard autonomy and advanced telemetry monitoring, but it also introduces new cybersecurity risks associated with adversarial machine learning (AML). Existing space cybersecurity frameworks provide general guidance, yet do not explicitly address ML specific threat models and interaction driven attack surfaces arising from onboard anomaly detection.

Keywords: Adversarial machine learning; Space cybersecurity; NewSpace; Anomaly detection; Cubesats (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ijocip:v:53:y:2026:i:c:s1874548226000235

DOI: 10.1016/j.ijcip.2026.100851

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