  A resilient and robust positioning, navigation, and timing (PNT) system is a
necessity for the navigation of autonomous vehicles (AVs). Global Navigation
Satelite System (GNSS) provides satellite-based PNT services. However, a
spoofer can temper an authentic GNSS signal and could transmit wrong position
information to an AV. Therefore, a GNSS must have the capability of real-time
detection and feedback-correction of spoofing attacks related to PNT receivers,
whereby it will help the end-user (autonomous vehicle in this case) to navigate
safely if it falls into any compromises. This paper aims to develop a deep
reinforcement learning (RL)-based turn-by-turn spoofing attack detection using
low-cost in-vehicle sensor data. We have utilized Honda Driving Dataset to
create attack and non-attack datasets, develop a deep RL model, and evaluate
the performance of the RL-based attack detection model. We find that the
accuracy of the RL model ranges from 99.99% to 100%, and the recall value is
100%. However, the precision ranges from 93.44% to 100%, and the f1 score
ranges from 96.61% to 100%. Overall, the analyses reveal that the RL model is
effective in turn-by-turn spoofing attack detection.
