A Reinforcement Learning Approach for Global Navigation Satellite System Spoofing Attack Detection in Autonomous Vehicles
A Reinforcement Learning Approach for Global Navigation Satellite System Spoofing Attack Detection in Autonomous Vehicles
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DOI：
10.1177/03611981221095509
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发表时间：
2021-08
影响因子：
1.7
通讯作者：
Sagar Dasgupta;T. Ghosh;Mizanur Rahman
中科院分区：
文献类型：
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作者：
Sagar Dasgupta;T. Ghosh;Mizanur Rahman
弹性定位、导航和定时（PNT）系统对于自主车辆（AV）的鲁棒导航是必要的。全球导航卫星系统提供基于卫星的PNT服务。然而，间谍可能篡改真实的GNSS信号，并可能向AV发送错误的位置信息。因此，AV必须具有实时检测与PNT接收器相关的欺骗攻击的能力，从而即使GNSS受到损害，它也将帮助最终用户（在这种情况下为AV）安全地导航。本文旨在利用低成本的车载传感器数据开发一种基于深度强化学习（RL）的逐向欺骗攻击检测方法。我们利用本田研究所驾驶数据集创建攻击和非攻击数据集，以开发深度RL模型，并评估了基于深度RL的攻击检测模型的性能。我们发现深度强化学习模型的准确率范围为99.99%到100%，召回值为100%。此外，精密度范围为93.44%至100%，f1评分范围为96.61%至100%。总的来说，分析表明，RL模型是有效的轮流欺骗攻击检测。
A resilient positioning, navigation, and timing (PNT) system is a necessity for the robust navigation of autonomous vehicles (AVs). A global navigation satellite system (GNSS) provides satellite-based PNT services. However, a spoofer can tamper the authentic GNSS signal and could transmit wrong position information to an AV. Therefore, an AV must have the capability of real-time detection of spoofing attacks related to PNT receivers, whereby it will help the end-user (the AV in this case) to navigate safely even if the GNSS is compromised. This paper aims to develop a deep reinforcement learning (RL)-based turn-by-turn spoofing attack detection method using low-cost in-vehicle sensor data. We have utilized the Honda Research Institute Driving Dataset to create attack and non-attack datasets to develop a deep RL model and have evaluated the performance of the deep RL-based attack detection model. We find that the accuracy of the deep RL model ranges from 99.99% to 100%, and the recall value is 100%. Furthermore, 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.