  In this study, a sensor fusion based GNSS spoofing attack detection framework
is presented that consists of three concurrent strategies for an autonomous
vehicle (AV): (i) prediction of location shift, (ii) detection of turns (left
or right), and (iii) recognition of motion state (including standstill state).
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. We have then combined k-Nearest Neighbors (k-NN) and
Dynamic Time Warping (DTW) algorithms to detect turns using data from the
steering angle sensor. In addition, data from an AV's speed sensor is used to
recognize the AV's motion state including the standstill state. To prove the
efficacy of the sensor fusion-based attack detection framework, attack datasets
are created for three unique and sophisticated spoofing attacks turn by turn,
overshoot, 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 three types of
spoofing attacks within the required computational latency threshold.
