Autonomous Vehicles (AVs) refer to systems capable of perceiving their states and moving without human intervention. Among the factors required for autonomous decision-making in mobility, positional awareness of the vehicle itself is the most critical. Accordingly, extensive research has been conducted on defense mechanisms against GPS spoofing attacks, which threaten AVs by disrupting position recognition. Among these, detection methods based on internal IMU sensors are regarded as some of the most effective. In this paper, we propose a spoofing attack system designed to neutralize IMU sensor-based detection. First, we present an attack modeling approach for bypassing such detection. Then, based on EKF sensor fusion, we experimentally analyze both the impact of GPS spoofing values on the internal target system and how our proposed methodology reduces anomaly detection within the target system. To this end, this paper proposes an attack model that performs GPS spoofing by stealing internal dynamic state information using an external IMU sensor, and the experimental results demonstrate that attack values can be injected without being detected.

翻译：自动驾驶车辆（AVs）指能够感知自身状态并在无人干预下自主移动的系统。在实现移动自主决策所需的诸多因素中，车辆自身的位置感知最为关键。因此，针对威胁自动驾驶车辆、通过干扰位置识别实施攻击的GPS欺骗行为，已有大量研究致力于开发相应的防御机制。其中，基于内部IMU传感器的检测方法被视为最有效的防御手段之一。本文提出了一种旨在中和基于IMU传感器检测的欺骗攻击系统。首先，我们提出了一种规避此类检测的攻击建模方法；随后基于扩展卡尔曼滤波（EKF）传感器融合技术，通过实验分析了GPS欺骗值对内部目标系统的影响，以及我们所提方法如何降低目标系统内的异常检测率。为此，本文构建了一种攻击模型：该模型通过外部IMU传感器窃取内部动态状态信息来实施GPS欺骗，实验结果表明攻击值可在不被检测的情况下成功注入系统。