A Reinforcement Learning Approach for GNSS Spoofing Attack Detection of Autonomous Vehicles

• URL: http://arxiv.org/abs/2108.08628v1
• Date: Thu, 19 Aug 2021 11:48:27 GMT
• Title: A Reinforcement Learning Approach for GNSS Spoofing Attack Detection of Autonomous Vehicles
• Authors: Sagar Dasgupta, Tonmoy Ghosh, Mizanur Rahman
• Abstract summary: This paper develops a deep reinforcement learning (RL)-based turn-by-turn spoofing attack detection using low-cost in-vehicle sensor data. We find that the accuracy of the RL model ranges from 99.99% to 100%, and the recall value is 100%. Overall, the analyses reveal that the RL model is effective in turn-by-turn spoofing attack detection.
• Score: 3.918774449495583
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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