无人机自主降落中木马漏洞的实验研究
An Experimental Study of Trojan Vulnerabilities in UAV Autonomous Landing
AI总结：
本研究通过实验揭示了无人机自主降落系统中木马攻击的漏洞，展示了攻击对模型准确性的严重影响，并提出了评估框架以识别受感染模型。
摘要：
本研究调查了城市空中交通（UAM）车辆自主导航和降落系统中的漏洞。具体而言，它专注于针对深度学习模型（如卷积神经网络（CNNs））的木马攻击。木马攻击通过在模型训练数据中嵌入隐秘触发器来工作。这些触发器在特定条件下导致特定故障，而模型在其他情况下仍能正常工作。我们使用DroNet框架评估了城市自主空中车辆（UAAVs）的漏洞。我们的实验显示，准确性显著下降，从干净数据上的96.4%降至受木马攻击数据触发后的73.3%。为了进行这项研究，我们收集了一个定制数据集并训练模型以模拟真实世界条件。我们还开发了一个评估框架，用于识别受木马感染的模型。本工作展示了木马攻击可能带来的安全风险，并为未来增强UAM系统韧性研究奠定了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
