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

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机 (UAV) 导航系统对新颖攻击向量的适应能力，这些攻击向量试图通过对感测数据进行微小更改来误导无人机。 此类系统容易受到不同的攻击，可能导致无人机被误导或被黑客攻击，带来重大安全风险。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此类隐形攻击的防御方法。将通过模拟和使用无人机测试台进行的真实实验来判断模型的性能，根据其抵抗攻击的能力。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，并在农作物监测、搜索救援和基础设施检查等各个领域具有潜在的应用前景。该项目符合 NSF 的使命，即通过出版物和开源软件分享其研究成果，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。该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开发和参与。这笔资金还将加强本科/研究生水平课程模块和自主证书的开发。该项目将调查结合两种触发器（即特洛伊触发器和对抗性触发器）的攻击场景，以创建一种恶意行为，使模型在没有攻击的情况下保持稳健。受到对抗性攻击的特洛伊木马，但当特洛伊木马存在时会表现出恶意行为。为了应对这些攻击，将开发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。安全部署也将受到调查，包括远程控制器和安全硬件。该奖项反映了 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。