FedLTN-CubeSat: Neuro-Symbolic Federated Learning for Intrusion Detection in LEO CubeSat Constellations

Low Earth Orbit (LEO) mega-constellations are becoming the backbone of global communications, yet their cybersecurity remains critically under-addressed. Intrusion detection systems (IDSs) for such constellations face a unique trilemma of accuracy, efficiency, and interpretability under extreme SWaP-C (size, weight, power, and cost) constraints. We present FedLTN-CubeSat (FedLTN refers to Federated Logic Tensor Networks), a neuro-symbolic federated learning framework for intrusion detection in LEO CubeSat constellations. The framework first employs a lightweight spatio-temporal separable perception encoder to efficiently extract features from telemetry and IQ data, designed to operate within the computational…

• S
  Strong
• NU
  National University of Defense Technology