Collaborative Research: SaTC: CORE: Small: UAV-NetSAFE.COM: UAV Network Security Assessment and Fidelity Enhancement through Cyber-Attack-Ready Optimized Machine-Learning Platforms

无人机(UAV)在民用、医疗保健和其他科学应用中得到了广泛的应用，如气候监测、灾害和流行病管理、商品运送、搜救行动和太空探索。UAV-NetSAFE.COM项目促进了人们对无人机网络的网络意识，开创了创新的安全解决方案，并通过直接减轻网络攻击的严重性来服务于美国国家利益，否则网络攻击可能会导致人员伤亡、敏感数据泄露和服务质量下降。这一合作项目通过研究预防、检测和缓解无人机网络攻击的多层安全框架来促进科学进步。此外，该项目还将影响其他社会关注的领域，如物联网和智能电网。它通过让学生参与教育和研究活动，如开发网络攻击模型，使用机器学习评估网络攻击，以及为值得信赖的网络设计硬件和软件解决方案，支持网络安全、机器学习和无人机网络领域的更广泛的教育。每年，该项目的成果将被整合到现有的和新的课程中，并展示以吸引高中生攻读STEM学位。这个合作项目的教育活动和跨学科研究工作由来自北达科他州的一名女性领导的PI领导，将使来自北达科他州的美国原住民学生以及来自芝加哥大都会和印第安纳州西北部的经济困难少数民族和代表性不足的学生受益。该项目由安全和值得信赖的网络空间计划和已建立的激励竞争研究计划(EPSCoR)联合资助。NSF SATC合作项目的首要目标是调查网络攻击对无人机网络和先锋网络攻击准备平台的影响。从软件的角度来看，无人机网络的网络攻击模型将被推导出来，以促进无人机独特的数据集，这些数据集有助于使用定性的风险调查和定量措施对无人机网络的网络攻击影响进行全面评估和后果评估。生成的数据集将用于通过采用先进的概率和统计机器学习算法，为无人机网络提供针对一系列网络攻击的攻击检测和决策协议。从硬件的角度来看，PIS将探索软件定义的无线电设置，这些设置将射频波束形成电路模块与基于软件的本地化和路径重新调度技术交织在一起，同时考虑到实际限制，如大小和结构复杂性。因此，该项目的主要贡献是开创了一个统一的框架，该框架需要对软件和硬件设置进行网络攻击评估、检测和对策。PIS将维护一个包罗万象的项目网站，帮助向工业界和研究界轻松传播网络攻击模型和对抗方法的数据集，以确保拟议的框架促进无人机通信和网络安全。该项目由安全和值得信赖的网络空间计划和既定的激励竞争研究计划(EPSCoR)联合资助。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。

Unmanned aerial vehicles (UAVs) find widespread uses in civil, healthcare, and other scientific applications, such as climate monitoring, disaster and pandemic management, merchandise delivery, search and rescue operations, and space exploration. The project UAV-NetSAFE.COM promotes cyber-awareness of UAV networks, pioneers innovative security solutions, and serves the US national interest by directly mitigating the severity of cyber-attacks that could otherwise lead to human causalities, leakage of sensitive data, and degraded quality-of-service. This collaborative project promotes science advancement by investigating a multilayer security framework for the prevention, detection, and mitigation of UAV-oriented cyber-attacks. Also, this project will also impact other areas of high societal interest, such as Internet-of-Things and smart grids. It supports broader education in the areas of cyber-security, machine-learning, and UAV networks by engaging students in educational and research activities such as developing cyber-attack models, evaluating cyber-attacks using machine learning, and designing hardware as well as software solutions for trustworthy networking. Every year, the outcomes of this project will be integrated into existing and new curricula and showcased to attract high school students into STEM degrees. Led by a female lead PI from UND, this collaborative project's educational activities and interdisciplinary research endeavors will benefit Native American students from the state of North Dakota and economically disadvantaged minority and underrepresented students from Chicago metropolitan and NW Indiana. This project is jointly funded by Secure and Trustworthy Cyberspace Program and the Established Program to Stimulate Competitive Research (EPSCoR). The overarching goal of this NSF SaTC collaborative project is to investigate the impacts of cyber-attacks on UAV networks and pioneer cyber-attack-ready platforms. From a software perspective, UAV networks' cyber-attack models will be derived to facilitate UAV-distinctive datasets that aid in the comprehensive assessment and aftermath evaluation of cyber-attack impacts on UAV networks employing qualitative risk investigations and quantitative measures. The resulting datasets will be used to empower UAV networks with both attack detection and decision-making protocols for a range of cyber-attacks by adopting advanced probabilistic and statistical machine-learning algorithms. From a hardware perspective, the PIs will explore software-defined radio setups that intertwine radio frequency beamforming circuit modules with software-based localization and path rescheduling techniques while considering practical constraints such as size and structural complexity. Therefore, the project's key contribution is to pioneer a unified framework that entails cyber-attack evaluation, detection, and countermeasures of software and hardware setups. The PIs will maintain an all-inclusive project website that will help easily disseminate the datasets of cyber-attack models and countermeasure methods to industry and research community to ensure that the proposed framework promotes UAV communication and network security. This project is jointly funded by Secure and Trustworthy Cyberspace Program and the Established Program to Stimulate Competitive Research (EPSCoR).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.