A Reinforcement Learning Approach for GNSS Spoofing Attack Detection of Autonomous Vehicles
This addresses the need for resilient positioning systems in autonomous vehicles to ensure safe navigation against spoofing threats, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of detecting GNSS spoofing attacks on autonomous vehicles by developing a deep reinforcement learning model using low-cost in-vehicle sensor data, achieving accuracy up to 100% and recall of 100% in evaluations.
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.