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

Lightweight AI can protect spacecraft without sacrificing performance: Logistic Regression achieved microsecond-level inference speeds while maintaining only a 1% accuracy drop compared to more complex Random Forest models, demonstrating that simpler machine learning approaches can effectively detect cyber threats on resource-constrained spacecraft systems.

Multiple types of cyber-RF attacks require detection: The research identifies five distinct threat categories affecting autonomous spacecraft—uplink jamming, Fake-NR spoofing, payload manipulation, ground-segment compromise, and unauthorized command injection—suggesting that spacecraft cybersecurity systems must be designed to recognize diverse attack patterns rather than single threat types.

Speed-accuracy trade-offs are critical for space systems: The study's focus on latency-accuracy analysis highlights a key tension in responsible AI deployment: faster threat detection is essential for autonomous spacecraft safety, but this speed cannot come at the cost of missing real attacks, requiring careful engineering choices about which model to deploy.

Theoretical rigor supports trustworthy AI decisions: The paper's use of physics-informed analysis (examining computational complexity, VC dimension, and Lipschitz continuity) demonstrates that responsible AI deployment benefits from mathematical foundations that can explain why models behave as they do, not just empirical performance metrics.

Edge AI requires continued innovation: The authors identify opportunities for improvement through "richer feature encoders and multi-timescale learning architectures," indicating that current TinyML solutions for spacecraft represent a starting point rather than a final answer, and ongoing research is needed to enhance both security and reliability.