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.

翻译：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安全漏洞。具体而言，研究聚焦于针对深度学习模型（如卷积神经网络，CNN）的木马攻击。木马攻击通过在模型的训练数据中嵌入隐蔽触发器来实现。这些触发器会在特定条件下引发特定的故障，而模型在其他情况下仍能保持正常性能。我们利用DroNet框架评估了城市自主飞行器（UAAV）的脆弱性。实验结果显示，模型准确率从干净数据上的96.4%显著下降至木马攻击触发数据上的73.3%。为开展此项研究，我们收集了定制数据集并训练模型以模拟真实场景。我们还开发了一个旨在识别感染木马模型的评估框架。这项工作揭示了木马攻击可能带来的安全风险，并为未来提升UAM系统鲁棒性的研究奠定了基础。