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

arXiv: https://arxiv.org/abs/2606.05779 · HTML: https://mlsanity.com/paper/2606.05779/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [DPFAGA-Dynamic Power Flow Analysis and Fault Characteristics: A Graph Attention Neural Network](https://mlsanity.com/paper/2503.15563/) (2025)
- [Artificial Intelligence Assisted Collaborative Edge Caching in Small Cell Networks](https://mlsanity.com/paper/2005.07941/) (2020)
- [User Preference Learning-Aided Collaborative Edge Caching for Small Cell Networks](https://mlsanity.com/paper/2005.07942/) (2020)
- [Privacy-Aware Framework of Robust Malware Detection in Indoor Robots: Hybrid Quantum Computing and Deep Neural Networks](https://mlsanity.com/paper/2510.13136/) (2025)
- [Quantum-Augmented AI/ML for O-RAN: Hierarchical Threat Detection with Synergistic Intelligence and Interpretability (Technical Report)](https://mlsanity.com/paper/2512.14742/) (2025)
- [Hybrid Quantum-Classical Encoding for Accurate Residue-Level pKa Prediction](https://mlsanity.com/paper/2603.11061/) (2026)

## Abstract (arXiv)

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.