无人机自动着陆系统易受隐藏触发的恶意攻击，导致精度骤降。
An Experimental Study of Trojan Vulnerabilities in UAV Autonomous Landing
- 在训练数据中植入隐蔽触发器，诱导模型在特定条件下失效
- 正常数据下准确率96.4%，受攻击后降至73.3%
- 为城市空中交通系统安全研究提供实证与评估框架
本研究调查了城市空中交通（UAM）飞行器自主导航与着陆系统的漏洞，重点关注针对深度学习模型（如卷积神经网络，CNN）的木马攻击。木马攻击通过在模型训练数据中嵌入隐蔽触发器，在特定条件下引发异常行为，而其他情况下仍保持正常性能。我们采用DroNet框架评估了城市自主空中车辆（UAAVs）的脆弱性。实验表明，干净数据上的准确率为96.4%，而受木马攻击触发的数据上准确率下降至73.3%。为开展研究，我们构建了自定义数据集并训练模型以模拟真实场景，同时开发了用于识别木马感染模型的评估框架。该工作揭示了木马攻击对UAM系统的潜在安全威胁，并为未来提升系统鲁棒性研究奠定基础。
原文摘要 · Abstract (English)
This study investigates the vulnerabilities of autonomous navigation and landing systems in Urban Air Mobility (UAM) vehicles. Specifically, it focuses on Trojan attacks that target deep learning models, such as Convolutional Neural Networks (CNNs). Trojan attacks work by embedding covert triggers within a model's training data. These triggers cause specific failures under certain conditions, while the model continues to perform normally in other situations. We assessed the vulnerability of Urban Autonomous Aerial Vehicles (UAAVs) using the DroNet framework. Our experiments showed a significant drop in accuracy, from 96.4% on clean data to 73.3% on data triggered by Trojan attacks. To conduct this study, we collected a custom dataset and trained models to simulate real-world conditions. We also developed an evaluation framework designed to identify Trojan-infected models. This work demonstrates the potential security risks posed by Trojan attacks and lays the groundwork for future research on enhancing the resilience of UAM systems.
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