Excellence in Research: Towards Secure Unmanned Aerial Vehicles-based Systems

该项目旨在提高基于深度学习的无人机(UAV)导航系统对新型攻击载体的弹性，这些攻击载体试图通过对传感数据进行微小更改来误导无人机。这类系统容易受到不同的攻击，这可能会导致无人机的误导或黑客攻击，构成重大安全风险。将考虑不同类型的对抗性攻击。此外，该项目将使用最先进的方法和实验来研究针对这些类型的隐形攻击的防御方法。这些模型的性能将根据它们通过模拟和使用无人机试验台进行的真实世界实验来抵御攻击的能力来判断。这项研究的结果将对开发安全可靠的无人机导航系统具有重要意义，在作物监测、搜救和基础设施检查等领域具有潜在的应用前景。该项目与NSF的使命相一致，即通过出版物和开源软件分享其发现，促进基础研究，促进国家利益，并为更广泛的科学界做出贡献。这项研究将支持北卡罗来纳A&T州立大学代表不足的研究生和本科生的技术开发和参与。这笔资金还将加强本科生/研究生级别的课程模块和自主证书的开发。该项目将调查组合了两个触发器(即特洛伊木马和对抗触发器)的攻击场景，以创建恶意行为，其中模型在没有木马的情况下在对手攻击下保持健壮，但在木马存在时表现出恶意行为。为了应对这些攻击，将开发一个基于深度学习的检测模型，该模型从对抗性扰动和反事实归因中学习特征，以检测和缓解这些攻击。安全部署也将被调查，包括遥控器和安全硬件。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to enhance the resilience of deep learning-based Unmanned Aerial Vehicles (UAV) navigation systems to novel attack vectors that attempt to misguide the UAV by making small changes to the sensed data. Such systems are vulnerable to different attacks, which can result in the misguidance or hacking of the UAV, posing significant safety risks. Different types of adversarial attacks will be considered. Additionally, the project will investigate defense methods against these types of stealthy attacks using state-of-the-art methods as well as experimentations. The performance of the models will be judged according to their ability to resist attacks through simulations and real-world experiments using a UAV testbed. The results of this research will have significant implications for the development of safe and reliable UAV navigation systems, with potential applications in various fields such as crop monitoring, search and rescue, and infrastructure inspection. The project aligns with NSF's mission to promote fundamental research, advance the national interest, and contribute to the broader scientific community by sharing its findings through publications and open-source software. The research will support technical development and engagement of underrepresented graduate and undergraduate students at North Carolina A&T State University. The funding will also enhance development of undergraduate/graduate-level course modules and certificates in autonomy.The project will investigate attack scenarios that combine two triggers, namely trojan and adversarial triggers, to create a malign behavior where the model remains robust in the absence of a trojan under adversarial attacks but behaves maliciously when the trojan is present. To address these attacks, a deep learning-based detection model will be developed that learns features from both adversarial perturbations and counterfactual attributions, to detect and mitigate these attacks. Secure deployment will also be investigated, including remote controller and secure hardware.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.