一句话：提出反馈驱动模糊测试框架 IMUFuzzer，自动发现无人机 IMU 信号注入攻击，并在 ArduPilot 上找到 23 个攻击。
论文研究机器人飞行器（RAV，尤其无人机）的传感器信号注入攻击：攻击者向 IMU 测量注入噪声可导致坠机等物理影响，但攻击依赖飞行器物理状态，且噪声与状态搜索空间巨大，难以自动发现。作者提出反馈驱动模糊测试框架 IMUFuzzer，利用高保真 RAV 模拟器生成真实 IMU 噪声，监控其对控制的影响以检测任务失败；生成多样任务路径覆盖攻击依赖的物理状态，并设计新颖反馈机制量化 RAV 韧性，引导搜索。在 ArduPilot 上发现 23 个成功信号注入攻击，评估反馈式传感器模糊测试的正确性与有效性，并通过物理实验验证攻击可行性。
Robotic aerial vehicles (RAVs), particularly drones, are crucial in civil and military sectors. However, researchers have found that adversaries can inject noise into sensor measurements and cause physical impacts on the RAVs like crashes. Although identifying such signal injection attacks is essential to evaluate and improve the robustness of an RAV, it is challenging to discover them since their impact depends on the RAV’s physical states and the search space of noise signals and physical states is vast due to its dynamic nature.This paper proposes IMUFUZZER, a feedback-driven fuzzing framework, to automatically test an RAVs system and discover signal injection attacks. IMUFUZZER generates realistic noise signals for various inertial measurement unit (IMU) sensors, and monitors their impact on RAV control to detect mission failures, leveraging a high-fidelity RAV simulator. To find the physical states that attacks depend on, IMUFUZZER generates various mission paths that the RAV will fly through. We develop a novel feedback mechanism to quantify the resilience of the RAV against attacks and efficiently guide the fuzzing process to find signal injection attacks. Using IMUFUZZER, we have discovered 23 successful signal injection attacks on popular RAV control software (ArduPilot). We evaluate the correctness and effectiveness of our feedback-based sensor fuzzing and demonstrate the feasibility of the discovered attacks through physical experiments.
DOI 原文 ·
@inproceedings{paperbot9300,
title = {IMUFuzzer: Resilience-based Discovery of Signal Injection Attacks on Robotic Aerial Vehicles},
author = {Sudharssan Mohan and Kyeongseok Yang and Zelun Kong and Yonghwi Kwon and Junghwan Rhee and Tyler Summers and Hongjun Choi and Heejo Lee and Chung Hwan Kim},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00134}
}