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

• URL: http://arxiv.org/abs/2608.17092v1
• Date: Mon, 17 Aug 2026 19:59:20 GMT
• Title: Structured Driving-State Narratives for Small Language Model-Based GNSS Spoofing Detection
• Abstract summary: This study develops a small language model (SLM)-based framework for detecting and classifying spoofing attacks.<n>The framework converts independent driving states from and other sensing sources into structured semantic narratives.<n>The framework can detect and classify spoofing attacks in real-time while requiring relatively low computational memory resources.
• Score: 8.560939383123658
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 identical training data and evaluated on the same test set. The evaluation considers five classes: no attack, overshoot attack, stopped attack, turn-by-turn attack, and wrong-turn attack. The framework is also evaluated with geographically unseen field data collected in Clemson, South Carolina, United States. Experimental results indicate that the evaluated SLMs achieve performance similar to the LLMs, achieving an average accuracy of 96.99%, precision of 99.05%, recall of 95.59%, and F1-score of 97.18%. In terms of computational efficiency and resource utilization, the SLMs demonstrate advantages over the LLMs by requiring lower inference latency and less GPU memory during both fine-tuning and inference. Evaluation using field data collected in a geographically distinct location further demonstrated its efficacy. The presented framework can detect and classify GNSS spoofing attacks in real-time while requiring relatively low computational and memory resources, and is therefore suitable for deployment on resource-constrained vehicular computing platforms.

Related papers

• LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications [76.41731220830714]
  Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external tools, and iterative prediction.<n>We examine applications in finance, weather, health, energy, and operations, and we summarize the benchmarks and datasets used for evaluation.
  arXiv  Detail & Related papers  (2026-08-24T10:02:39Z)
• High-Order Liquid Evidence Encoding for Gradual GNSS Spoofing Detection in Autonomous Driving [6.820140586466226]
  Spoofing attacks can manipulate vehicle position estimates.<n>Existing methods often rely on static vehicle-behavior features.<n>We propose a causal high-order liquid evidence framework for spoofing detection.
  arXiv  Detail & Related papers  (2026-08-12T08:30:17Z)
• Development of Vision-Language Model-based GNSS Spoofing Detection for Autonomous Vehicle Navigation [10.15536470870511]
  We develop the first Vision-Language Model (VLM)-based framework for spoofing detection for autonomous vehicles.<n>Our approach introduces a three-stage fine-tuning process that first grounds visual cues, and then calibrates sensor data.<n>Results show that our VLM-based approach correctly classified every wrong-turn and stop attacks, and attains 88%-93% accuracy for overshoot attacks.
  arXiv  Detail & Related papers  (2026-07-27T03:20:08Z)
• Region Matters: Efficient and Reliable Region-Aware Visual Place Recognition [46.97190804063947]
  We introduce FoL++, a method combining robust discriminative region modeling with adaptive re-ranking.<n>FoL++ achieves state-of-the-art performance with a lightweight memory footprint, improving inference speed by 40% over FoL.
  arXiv  Detail & Related papers  (2026-04-24T09:28:35Z)
• GNSS Spoofing Detection Based on Opportunistic Position Information [1.9688858888666714]
  Position-based Attack Detection Scheme (PADS) is a probabilistic framework that uses regression and uncertainty analysis for positions.<n>PADS achieves up to 3 times the true positive rate at a low false positive rate.
  arXiv  Detail & Related papers  (2025-06-14T17:27:06Z)
• Experimental Validation of Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles [5.624009710240032]
  We present a sensor fusion-based spoofing attack detection framework for Autonomous Vehicles. Experiments are conducted in Tuscaloosa, AL, mimicking urban road structures. Results demonstrate the framework's ability to detect various sophisticated spoofing attacks, even including slow drifting attacks.
  arXiv  Detail & Related papers  (2024-01-02T17:30:46Z)
• Revisiting Few-Shot Object Detection with Vision-Language Models [49.79495118650838]
  We revisit the task of few-shot object detection (FSOD) in the context of recent foundational vision-language models (VLMs) We propose Foundational FSOD, a new benchmark protocol that evaluates detectors pre-trained on any external data. We discuss our recent CVPR 2024 Foundational FSOD competition and share insights from the community.
  arXiv  Detail & Related papers  (2023-12-22T07:42:00Z)
• Automated classification of pre-defined movement patterns: A comparison between GNSS and UWB technology [55.41644538483948]
  Real-time location systems (RTLS) allow for collecting data from human movement patterns. The current study aims to design and evaluate an automated framework to classify human movement patterns in small areas.
  arXiv  Detail & Related papers  (2023-03-10T14:46:42Z)
• On the Effective Usage of Priors in RSS-based Localization [56.68864078417909]
  We propose a Received Signal Strength (RSS) fingerprint and convolutional neural network-based algorithm, LocUNet. In this paper, we study the localization problem in dense urban settings. We first recognize LocUNet's ability to learn the underlying prior distribution of the Rx position or Rx and transmitter (Tx) association preferences from the training data, and attribute its high performance to these.
  arXiv  Detail & Related papers  (2022-11-28T00:31:02Z)
• Higher Performance Visual Tracking with Dual-Modal Localization [106.91097443275035]
  Visual Object Tracking (VOT) has synchronous needs for both robustness and accuracy. We propose a dual-modal framework for target localization, consisting of robust localization suppressingors via ONR and the accurate localization attending to the target center precisely via OFC.
  arXiv  Detail & Related papers  (2021-03-18T08:47:56Z)
• Prediction-Based GNSS Spoofing Attack Detection for Autonomous Vehicles [5.579370215490055]
  We have developed a prediction-based spoofing attack detection strategy using the long short-term memory (LSTM) model. Based on the predicted distance traveled between the current location and the immediate future location, a threshold value is established. Our analysis revealed that the prediction-based spoofed attack detection strategy can successfully detect the attack in real-time.
  arXiv  Detail & Related papers  (2020-10-16T18:26:59Z)
• Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection [121.28769542994664]
  Domain adaptation for object detection tries to adapt the detector from labeled datasets to unlabeled ones for better performance. In this paper, we are the first to reveal that the region proposal network (RPN) and region proposal classifier(RPC) demonstrate significantly different transferability when facing large domain gap.
  arXiv  Detail & Related papers  (2020-09-17T07:39:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.