  This paper presents a sensor fusion based Global Navigation Satellite System
(GNSS) spoofing attack detection framework for autonomous vehicles (AV) that
consists of two concurrent strategies: (i) detection of vehicle state using
predicted location shift -- i.e., distance traveled between two consecutive
timestamps -- and monitoring of vehicle motion state -- i.e., standstill/ in
motion; and (ii) detection and classification of turns (i.e., left or right).
Data from multiple low-cost in-vehicle sensors (i.e., accelerometer, steering
angle sensor, speed sensor, and GNSS) are fused and fed into a recurrent neural
network model, which is a long short-term memory (LSTM) network for predicting
the location shift, i.e., the distance that an AV travels between two
consecutive timestamps. This location shift is then compared with the
GNSS-based location shift to detect an attack. We have then combined k-Nearest
Neighbors (k-NN) and Dynamic Time Warping (DTW) algorithms to detect and
classify left and right turns using data from the steering angle sensor. To
prove the efficacy of the sensor fusion-based attack detection framework,
attack datasets are created for four unique and sophisticated spoofing
attacks-turn-by-turn, overshoot, wrong turn, and stop, using the publicly
available real-world Honda Research Institute Driving Dataset (HDD). Our
analysis reveals that the sensor fusion-based detection framework successfully
detects all four types of spoofing attacks within the required computational
latency threshold.
