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.

验证传感器融合型 GNSS 欺骗攻击检测框架在自动驾驶车辆中的性能，并使用两种策略进行检测：通过传感器融合和机器学习来判断位置变化，以及使用机器学习模型检测和分类转弯。通过实验证明该框架能够有效地检测各种复杂的 GNSS 欺骗攻击。