Structured Driving-State Narratives for Small Language Model-Based GNSS Spoofing Detection
Autonomous vehicles (AVs) depend on reliable Global Navigation Satellite System (GNSS) positioning. However, spoofed GNSS signals can induce plausible but incorrect vehicle states. This study develops a small language model (SLM)-based framework for detecting and classifying GNSS spoofing attacks by comparing vehicle behaviors independently derived from GNSS and other sensing sources. The framework converts independent driving states from GNSS and other sensing sources into structured semantic narratives that are provided to an SLM for spoofing detection and attack classification. The performance of the SLM-based framework is compared with large language models (LLMs) fine-tuned on identi...
| Authors | Abyad Enan, Sagar Dasgupta, Mizanur Rahman et al. |
|---|---|
| Primary category | cs.CR |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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