Experimental Validation of Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles

The paper by Dasgupta, Shakib, and Rahman validates a sensor fusion-based Global Navigation Satellite System (GNSS) spoofing attack detection framework for Autonomous Vehicles (AVs). The framework uses inertial sensor data and a long short-term memory (LSTM) neural network to predict location shift and detect spoofing attacks. The study also employs a Random-Forest supervised machine learning model to detect and classify turns. The framework effectively detected various GNSS spoofing attacks in experiments conducted in Tuscaloosa, AL.

Publication date: 4 Jan 2024
Project Page: Not provided
Paper: https://arxiv.org/pdf/2401.01304