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TinyML models analyzed for spacecraft cybersecurity

By PulseAugur Editorial · [2 sources] ·

A new research paper analyzes the performance of TinyML models for cybersecurity threats on autonomous spacecraft. The study focuses on the latency-accuracy trade-offs of classical machine learning models like Random Forest and Logistic Regression when detecting various cyber-RF attacks. Results indicate that Logistic Regression offers microsecond-level inference with a minimal accuracy decrease, making it a suitable baseline for onboard spacecraft autonomy. AI

IMPACT This research could lead to more efficient and secure onboard AI systems for autonomous spacecraft.

RANK_REASON The cluster contains an academic paper detailing a new analysis of TinyML models for a specific application.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Van Le, Trevor Tran, Tan Le ·

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

    arXiv:2606.05779v1 Announce Type: cross Abstract: 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…

  2. arXiv stat.ML TIER_1 English(EN) · Tan Le ·

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

    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 detecti…

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