arXiv:2609.26231v1 Announce Type: new
Abstract: Continuous and subtle GNSS spoofing poses a serious threat to autonomous vehicles because forged positions may remain locally plausible while gradually...
By Muhammad Ayub Sabir, Junbiao Pang, Fatima Ashraf
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.
By Abyad Enan, Sagar Dasgupta, Mizanur Rahman, Mashrur Chowdhury
The paper investigates whether passive motion traces recorded during selfie capture can serve as an auxiliary signal for detecting spoofing and verifying users in mobile remote identity verification systems. It introduces the CanSelfie dataset, comprising 375 multi‑sensor sequences from 30 participants, and evaluates seven time‑series classifiers and eight anomaly detectors across various sensor configurations. Results show that accelerometer‑only classifiers achieve very low false rejection rates, while certain models achieve low false acceptance rates and high verification accuracy, indicating that selfie‑capture motion is a promising low‑friction evidence channel.
By Erkka Rantahalvari, Olli Silv\'en, Zinelabidine Boulkenafet, Constantino \'Alvarez Casado
arXiv:2506. 21129v2 Announce Type: replace-cross Abstract: Autonomous unmanned aerial vehicles (UAVs) increasingly rely on reinforcement learning (RL) for navigation.
By Deepak Kumar Panda, Adolfo Perrusquia, Weisi Guo
arXiv:2601. 03040v2 Announce Type: replace-cross Abstract: A fundamental requirement for full autonomy is the ability to sustain accurate navigation in the absence of external data, such as GNSS signals or visual information.
By Arup Kumar Sahoo, Itzik Klein
The paper introduces SPADE, a labelled, multi‑modal dataset for detecting attacks on Signal Phase and Timing (SPaT) messages from the perspective of connected vehicles. Generated via Eclipse MOSAIC, SPADE includes 1.89 million timestep records across six attack classes and one benign class, combining SPaT fields, camera confidence scores, and V2V peer data over 40 features. The dataset, along with generation code and scenario configurations, is publicly released on GitHub to enable reproducible deep‑learning intrusion detection research in C‑V2X security.
By James Di Novo, Hany Ragab, Sylvain P. Leblanc