TinyML-Driven Cybersecurity for Autonomous Spacecraft: Latency-Accuracy Analysis for SPARTA RF and Cyber Threat Detection

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

This study presents a physics-informed theoretical and empirical analysis of TinyML-compatible classical models for detecting SPARTA cyber-RF threats onboard autonomous spacecraft. Researchers evaluated Logistic Regression, SVM, Random Forest, and Multi-Layer Perceptrons against uplink jamming, Fake-NR spoofing, payload manipulation, ground-segment compromise, and unauthorized command injection. The methodology combined theoretical guarantees on computational complexity and VC dimension with empirical measurements on adversarial RF spectrograms generated via BandErasure, FakeNR, and NoiseBurst corruption. Results show Logistic Regression achieves microsecond-level inference (69.5 µs) with only a 1% accuracy drop (0.94) compared to Random Forest (0.950), which has a prohibitive 7.3 ms latency. The Multi-Layer Perceptron offers the best balance, achieving the highest macro F1-score (0.824) with a feasible 99.6 µs inference time. All models exhibited persistent weaknesses in detecting payload manipulation and ground-segment compromise.

Key takeaway

For Machine Learning Engineers designing onboard cybersecurity for autonomous spacecraft, you must prioritize the latency-accuracy trade-off. While Logistic Regression offers microsecond-level inference (69.5 µs) for high-energy threats, its 0.798 macro F1-score on subtle attacks like payload manipulation is a significant risk. You should consider Multi-Layer Perceptrons, which achieve a superior 0.824 macro F1-score with a feasible 99.6 µs latency. Future efforts should focus on developing richer feature encoders to address persistent weaknesses in detecting payload manipulation and ground-segment compromise.

Key insights

TinyML for autonomous spacecraft cybersecurity demands balancing microsecond latency with detection accuracy, favoring Logistic Regression as a baseline and MLP for optimal trade-offs.

Principles

• Linear models excel in speed, struggle with subtle threats.
• Nonlinear models offer accuracy, incur higher latency.
• Payload manipulation is inherently difficult to detect via RF spectrograms.

Method

Evaluate TinyML models by combining physics-informed theoretical analysis of computational complexity and VC dimension with empirical testing on adversarial RF spectrograms.

In practice

• Use Logistic Regression for fastest onboard threat detection.
• Consider MLP for optimal accuracy-latency balance.
• Prioritize richer feature encoders for subtle threats.

Topics

• TinyML
• Spacecraft Cybersecurity
• SPARTA Threat Model
• RF Threat Detection
• Latency-Accuracy Analysis
• Embedded Machine Learning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.