Autonomous spacecraft require rapid, lightweight, and reliable onboard detection of cyber-RF threats. Using the SPARTA attack model, we analyze the latency-accuracy trade-offs of TinyML-compatible classical models -- Random Forest, Logistic Regression, SVM, and MLP -- for detecting uplink jamming, Fake-NR spoofing, payload manipulation, ground-segment compromise, and unauthorized command injection. We present a physics-informed theoretical analysis of each model's computational complexity, VC dimension, Lipschitz continuity, and latency scaling, supported by empirical measurements on adversarial RF spectrograms generated via BandErasure, FakeNR, and NoiseBurst corruption modes. Results show that Logistic Regression achieves microsecond-level inference with only a 1\% accuracy drop relative to Random Forest, making it an effective TinyML baseline for onboard autonomy. The study also identifies opportunities for advancing spacecraft cybersecurity through richer feature encoders and multi-timescale learning architectures, building on recent progress in edge intelligence and trustworthy AI.

翻译：自主航天器需要快速、轻量且可靠的在轨网络-射频威胁检测。本文基于SPARTA攻击模型，分析了兼容TinyML的经典模型（随机森林、逻辑回归、支持向量机和多层感知机）在检测上行链路干扰、Fake-NR欺骗、有效载荷操纵、地面段入侵和未授权指令注入时的延迟-准确性权衡。我们提出了一种基于物理理论的分析框架，涵盖各模型的计算复杂度、VC维度、Lipschitz连续性及延迟缩放特性，并通过基于BandErasure、FakeNR和NoiseBurst干扰模式生成的对抗性射频频谱图进行实验验证。结果表明，逻辑回归可实现微秒级推理，且相比随机森林仅损失1%的准确率，使其成为适用于在轨自主性的高效TinyML基线。本研究还基于边缘智能与可信AI的最新进展，为通过更丰富的特征编码器及多时间尺度学习架构推进航天器网络安全指出了方向。