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

📰 ArXiv cs.AI

Learn how TinyML-driven cybersecurity can enhance autonomous spacecraft security through latency-accuracy analysis of classical machine learning models for cyber threat detection

advanced Published 5 Jun 2026

Action Steps

Apply TinyML-compatible models to detect cyber-RF threats in autonomous spacecraft
Analyze latency-accuracy trade-offs of classical models like Random Forest and Logistic Regression
Configure SPARTA attack model for simulating cyber threats
Test and evaluate the performance of different models for detecting various types of cyber attacks
Optimize model selection based on latency and accuracy requirements

Who Needs to Know This

Cybersecurity teams and spacecraft engineers can benefit from this research to develop more efficient and reliable onboard threat detection systems. This knowledge can help them make informed decisions about model selection and optimization for autonomous spacecraft security

Key Insight

💡 TinyML-compatible classical models can provide a good balance between latency and accuracy for onboard cyber threat detection in autonomous spacecraft

Key Takeaways

Learn how TinyML-driven cybersecurity can enhance autonomous spacecraft security through latency-accuracy analysis of classical machine learning models for cyber threat detection

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