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

卓越的研究：迈向安全的基于无人机的系统

• 批准号: 2301553
• 负责人: Mahmoud Mahmoud
• 金额: $ 57.59万
• 依托单位: North Carolina Agricultural & Technical State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2301553&HistoricalAwards=false
• 关键词: Excellence; Research; Towards; Secure; Unmanned; Excellence; Research; Towards; Secure; Unmanned

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.

该项目旨在增强基于深度学习的无人驾驶汽车（UAV）导航系统的弹性，以通过对感知的数据进行小型更改来试图误导无人机的新型攻击媒介。 这样的系统容易受到不同攻击的影响，这可能导致无人机的误导或黑客攻击，从而带来很大的安全风险。将考虑不同类型的对抗攻击。此外，该项目将使用最先进的方法和实验调查针对这些类型的隐形攻击的防御方法。模型的性能将根据其通过使用UAV测试床进行模拟和现实世界实验来抵抗攻击的能力来判断。这项研究的结果将对安全可靠的无人机导航系统的开发产生重大影响，并在各个领域（例如作物监测，搜救和救援以及基础设施检查）中进行了潜在的应用。该项目符合NSF促进基础研究，提高国家利益的使命，并通过通过出版物和开源软件分享其发现来为更广泛的科学界做出贡献。这项研究将支持北卡罗来纳州A＆T州立大学代表性不足的研究生和本科生的技术发展和参与。资金还将增强自主权中的本科/研究生级课程模块和证书的发展。该项目将调查结合两个触发因素的攻击场景，即特洛伊木马和对抗性触发因素，以创建一个模型，使该模型在trojan中保持强大的态度，但在越野式攻击下，却表现出了trocajan的行为。为了解决这些攻击，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，以检测和减轻这些攻击。安全部署还将进行调查，包括遥控器和安全硬件。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估。