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.
