轻量级模型在航天器上实现毫秒级威胁检测，精度损失仅1%。
TinyML-Driven Cybersecurity for Autonomous Spacecraft: Latency-Accuracy Analysis for SPARTA RF and Cyber Threat Detection
- 选用轻量级模型，结合物理特性分析计算复杂度与延迟
- 逻辑回归实现微秒级推理，精度仅比随机森林低1%
- 适合对实时性要求高的航天器自主安全系统
自主航天器需要快速、轻量且可靠的机载网络-射频威胁检测能力。基于SPARTA攻击模型，本文分析了适用于TinyML的经典模型（随机森林、逻辑回归、SVM、MLP）在检测上行干扰、假NR欺骗、载荷篡改、地面段入侵及非法指令注入等威胁时的延迟-精度权衡。通过理论分析各模型的计算复杂度、VC维、Lipschitz连续性与延迟缩放规律，并结合BandErasure、FakeNR、NoiseBurst等对抗性射频谱图的实测数据验证。结果表明，逻辑回归在仅牺牲1%精度的前提下，可实现微秒级推理，成为机载自主系统的有效轻量基准。研究还指出，通过更丰富的特征编码器与多时间尺度学习架构，有望进一步提升航天器网络安全水平，依托边缘智能与可信AI最新进展。
原文摘要 · Abstract (English)
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.
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