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arXiv AI By Abyad Enan, Sagar Dasgupta, Mizanur Rahman, Mashrur Chowdhury

Structured Driving-State Narratives for Small Language Model-Based GNSS Spoofing Detection

Read the original on arXiv AI →

The paper introduces a small language model (SLM)-based framework that detects and classifies GNSS spoofing attacks on autonomous vehicles by converting vehicle states from GNSS and other sensors into structured semantic narratives. The SLM achieves performance comparable to large language models, with an average accuracy of 96.99%, while offering lower inference latency and reduced GPU memory usage. Field tests in Clemson, South Carolina, confirm the framework’s real‑time detection capabilities on resource‑constrained vehicular platforms.

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 AI.

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