A Sensor Fusion-Based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles
A Sensor Fusion-Based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles
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DOI：
10.1109/tits.2022.3197817
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发表时间：
2022-08-26
影响因子：
8.5
通讯作者：
Chowdhury, Mashrur
中科院分区：
文献类型：
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作者：
Dasgupta, Sagar;Rahman, Mizanur;Chowdhury, Mashrur
本文提出了一种用于自动驾驶汽车（AV）的基于传感器融合的全球导航卫星系统（GNSS）欺骗攻击检测框架，该框架由两种策略组成：（i）比较预测位置偏移（即两个连续时间戳之间的行驶距离）和基于惯性传感器的位置偏移，以及监测车辆运动状态（即静止/运动）； (ii) 转弯（左转或右转）的检测和分类以及车辆运动状态的检测。在第一种策略中，来自低成本车载惯性传感器（即速度计、加速度计和转向角传感器）的数据被融合并输入到长短期记忆（LSTM）神经网络，以预测自动驾驶汽车在两个连续时间戳之间行驶的距离。第二种策略结合了 k 最近邻 (k-NN) 和动态时间规整 (DTW) 算法来检测转弯，然后使用转向角传感器输出对左转弯和右转弯进行分类。在这两种策略中，将 GNSS 得出的速度与速度计输出进行比较，以提高本文提出的框架的有效性。为了证明基于传感器融合的攻击检测框架的有效性，使用公开的真实世界本田研究所驾驶数据集 (HDD)，为四种独特的欺骗攻击场景（逐向、超调、错误转向和停止）创建了攻击数据集。本研究中进行的分析表明，基于传感器融合的检测框架在所需的计算延迟阈值内成功检测到所有四种类型的欺骗攻击。
This paper presents a sensor fusion-based Global Navigation Satellite System (GNSS) spoofing attack detection framework for autonomous vehicles (AVs) that consists of two strategies: (i) comparison between predicted location shift-i.e., distance traveled between two consecutive timestamps-and inertial sensor based location shift in addition to monitoring of vehicle motion states-i.e., standstill/ in motion; and (ii) detection and classification of turns (left or right) along with detection of vehicle motion states. In the first strategy, data from low-cost in-vehicle inertial sensors-i.e., speedometer, accelerometer, and steering angle sensor-are fused and fed to a long short-term memory (LSTM) neural network to predict the distance an AV will travel between two consecutive timestamps. The second strategy combines k-Nearest Neighbors (k-NN) and Dynamic Time Warping (DTW) algorithms to detect a turn and then classify left and right turns using steering angle sensor output. In both strategies, the GNSS-derived speed is compared with speedometer output to improve the effectiveness of the framework presented in this paper. To prove the efficacy of the sensor fusion-based attack detection framework, attack datasets are created for four unique spoofing attack scenarios-turn-by-turn, overshoot, wrong turn, and stop, using the publicly available real-world Honda Research Institute Driving Dataset (HDD). Analyses conducted in this study reveal that the sensor fusion-based detection framework successfully detects all four types of spoofing attacks within the required computational latency threshold.