  Global Navigation Satellite System (GNSS) provides Positioning, Navigation,
and Timing (PNT) services for autonomous vehicles (AVs) using satellites and
radio communications. Due to the lack of encryption, open-access of the coarse
acquisition (C/A) codes, and low strength of the signal, GNSS is vulnerable to
spoofing attacks compromising the navigational capability of the AV. A spoofed
attack is difficult to detect as a spoofer (attacker who performs spoofing
attack) can mimic the GNSS signal and transmit inaccurate location coordinates
to an AV. In this study, we have developed a prediction-based spoofing attack
detection strategy using the long short-term memory (LSTM) model, a recurrent
neural network model. The LSTM model is used to predict the distance traveled
between two consecutive locations of an autonomous vehicle. In order to develop
the LSTM prediction model, we have used a publicly available real-world
comma2k19 driving dataset. The training dataset contains different features
(i.e., acceleration, steering wheel angle, speed, and distance traveled between
two consecutive locations) extracted from the controlled area network (CAN),
GNSS, and inertial measurement unit (IMU) sensors of AVs. Based on the
predicted distance traveled between the current location and the immediate
future location of an autonomous vehicle, a threshold value is established
using the positioning error of the GNSS device and prediction error (i.e.,
maximum absolute error) related to distance traveled between the current
location and the immediate future location. Our analysis revealed that the
prediction-based spoofed attack detection strategy can successfully detect the
attack in real-time.
