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arXiv Machine Learning By Kyle Stein, Guillermo Francia III, Eman El-Sheikh, Hossain Shahriar

Temporal and Multimodal Deep Learning for Cyberattack Detection in LEO Satellite Systems

Read the original on arXiv Machine Learning →

The paper presents a deep‑learning approach for detecting cyberattacks in Low‑Earth Orbit satellite systems, leveraging the UNSW‑IoTSAT dataset. It explores structured architectures that preserve hardware, orbital, and radio‑frequency data, including a Subsystem‑Fusion MLP and a hierarchical multimodal Transformer that captures cross‑subsystem interactions and temporal dynamics. Experiments show that the hierarchical Transformer achieves up to 91.66% accuracy and 85.63% macro F1 under a leakage‑resistant evaluation protocol, highlighting the importance of multimodal modeling and rigorous testing.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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