In this paper, we validate the performance of the a sensor fusion-based Global Navigation Satellite System (GNSS) spoofing attack detection framework for Autonomous Vehicles (AVs). To collect data, a vehicle equipped with a GNSS receiver, along with Inertial Measurement Unit (IMU) is used. The detection framework incorporates two strategies: The first strategy involves comparing the predicted location shift, which is the distance traveled between two consecutive timestamps, with the inertial sensor-based location shift. For this purpose, data from low-cost in-vehicle inertial sensors such as the accelerometer and gyroscope sensor are fused and fed into a long short-term memory (LSTM) neural network. The second strategy employs a Random-Forest supervised machine learning model to detect and classify turns, distinguishing between left and right turns using the output from the steering angle sensor. In experiments, two types of spoofing attack models: turn-by-turn and wrong turn are simulated. These spoofing attacks are modeled as SQL injection attacks, where, upon successful implementation, the navigation system perceives injected spoofed location information as legitimate while being unable to detect legitimate GNSS signals. Importantly, the IMU data remains uncompromised throughout the spoofing attack. To test the effectiveness of the detection framework, experiments are conducted in Tuscaloosa, AL, mimicking urban road structures. The results demonstrate the framework's ability to detect various sophisticated GNSS spoofing attacks, even including slow position drifting attacks. Overall, the experimental results showcase the robustness and efficacy of the sensor fusion-based spoofing attack detection approach in safeguarding AVs against GNSS spoofing threats.

翻译：本文验证了一种基于传感器融合的全球导航卫星系统（GNSS）欺骗攻击检测框架在自动驾驶车辆（AVs）中的性能。为采集数据，采用了配备GNSS接收器和惯性测量单元（IMU）的车辆。该检测框架包含两种策略：第一种策略是将预测的位置偏移（即两个连续时间戳之间行驶的距离）与基于惯性传感器的位置偏移进行比较。为此，我们将低成本车载惯性传感器（如加速度计和陀螺仪）的数据进行融合，并输入长短期记忆（LSTM）神经网络。第二种策略采用随机森林监督机器学习模型，利用转向角传感器的输出检测并分类转弯动作，区分左转和右转。实验中模拟了两种欺骗攻击模型：逐转弯攻击和错误转弯攻击。这些欺骗攻击被建模为SQL注入攻击，成功实施后，导航系统会将注入的虚假位置信息视为合法信号，同时无法检测到真实的GNSS信号。重要的是，在欺骗攻击过程中，IMU数据始终保持未受损状态。为测试检测框架的有效性，我们在阿拉巴马州塔斯卡卢萨市的城市道路结构模拟环境中进行了实验。结果表明，该框架能够检测各种复杂的GNSS欺骗攻击，甚至包括缓慢位置漂移攻击。总体而言，实验验证了基于传感器融合的欺骗攻击检测方法在保护自动驾驶车辆免受GNSS欺骗威胁方面的鲁棒性和有效性。