Autonomous vehicles increasingly rely on high-end multi-frequency GNSS receivers that track modernized civil signals, such as L2C, L5, and L1C, alongside legacy GPS L1~C/A. Although multi-frequency operation improves robustness against natural error sources, its resilience against synchronous multi-band spoofing attack...
M. Ahmad, Muhammad Sami Irfan, Sagar Dasgupta et al.· ION GNSS+, The International...· 0 citations
State departments of transportation (DOTs) in the United States increasingly rely on statewide continuously operating reference station (CORS) networks to support high-precision Global Navigation Satellite System (GNSS)-based positioning and timing for intelligent transportation systems. These networks also provide con...
M. Ahmad, Sagar Dasgupta, Muhammad Sami Irfan et al.· 0 citations
LLMSec-AV introduces an AV-specific, machine-readable vulnerability taxonomy for weakness discovery and shows that LLMs can complement conventional analyzers by identifying and organizing safety-relevant findings in real AV software.
The results show that build integration, not candidate generation or fuzzing, is the primary barrier to reliable LLM-assisted dynamic analysis of full autonomous-vehicle software stacks.
This paper develops the first Vision-Language Model (VLM)-based framework for GNSS spoofing detection for autonomous vehicles by fusing front-camera visual data with in-vehicle sensor readings against GNSS-derived maneuvers.
Mohammed Aldeen, Muhammad Sami Irfan, Sagar Dasgupta et al.· arXiv.org· 0 citations
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.
Abyad Enan, Sagar Dasgupta, Mizanur Rahman et al.· 0 citations
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