A Sensor Fusion-Based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles

Abstract

This paper presents a sensor fusion-based Global Navigation Satellite System (GNSS) spoofing attack detection framework for autonomous vehicles (AVs) that consists of two strategies: (i) comparison between predicted location shift—i.e., distance traveled between two consecutive timestamps—and inertial sensor based location shift in addition to monitoring of vehicle motion states—i.e., standstill/ in motion; and (ii) detection and classification of turns (left or right) along with detection of vehicle motion states. In the first strategy, data from low-cost in-vehicle inertial sensors—i.e., speedometer, accelerometer, and steering angle sensor—are fused and fed to a long short-term memory (LSTM) neural network to predict the distance an AV will travel between two consecutive timestamps. The second strategy combines k-Nearest Neighbors (k-NN) and Dynamic Time Warping (DTW) algorithms to detect a turn and then classify left and right turns using steering angle sensor output. In both strategies, the GNSS-derived speed is compared with speedometer output to improve the effectiveness of the framework presented in this paper. To prove the efficacy of the sensor fusion-based attack detection framework, attack datasets are created for four unique spoofing attack scenarios—turn-by-turn, overshoot, wrong turn, and stop, using the publicly available real-world Honda Research Institute Driving Dataset (HDD). Analyses conducted in this study reveal that the sensor fusion-based detection framework successfully detects all four types of spoofing attacks within the required computational latency threshold.