Crii: satc: muse: multi-modal security enhancement for cyber resilient UAV operation

Federal Grant Award Summary

The National Science Foundation's Division of Computer and Network Systems awarded the University of Nebraska at Omaha a $175,000 Computer and Information Science and Engineering (CFDA 47.070) project grant effective October 1, 2025, through September 30, 2027. This award funds research and development of the Multi-Modal Security Enhancement (MUSE) framework, an artificial intelligence-powered navigation and localization system designed to enable unmanned aerial vehicles (UAVs) to operate securely in GPS-denied or adversarial environments. The project delivers a multi-modal localization module and cyber-resilient path planning system that integrates sensor data from Light Detection and Ranging (LIDAR) sensors, cameras, and motion sensors to achieve real-time localization and GPS spoofing detection without reliance on traditional GPS navigation.

The grant supports the development and validation of novel methods in sensor fusion, trajectory reconstruction, and secure autonomous navigation, with deliverables to be evaluated on the Multi-Modal AI-Enabled UAV Research (MM-AIR) testbed under diverse operating conditions. Beyond technical research outputs, the award includes educational and outreach components providing training opportunities in cybersecurity for undergraduate, graduate, and high school students. The project's broader impact addresses the need for secure and trustworthy cyber-physical infrastructure to enhance the resilience and reliability of critical aerial services used in emergency response, infrastructure inspection, and environmental monitoring applications.

Abstract

Unmanned aerial vehicles (UAVS), commonly known as drones, are increasingly used in critical applications such as emergency response, infrastructure inspection, and environmental monitoring. However, these systems are highly vulnerable to cyber threats, especially global positioning system (GPS) spoofing attacks that can mislead navigation and cause serious consequences. The project?S novelties are the development of an AI-powered, GPS-independent navigation and localization framework that enables UAVS to operate securely and autonomously in GPS-denied or adversarial environments. The project's broader significance and importance are in contributing to a secure and trustworthy cyber-physical infrastructure, enhancing preparedness, and improving the resilience of essential aerial services. This project also includes outreach activities and training opportunities for undergraduate, graduate, and high school students in cybersecurity. The research focuses on designing a multi-modal security enhancement (MUSE) framework that integrates sensor data from light detection and ranging (LIDAR) sensors, cameras, and other motion sensors to achieve real-time localization and spoofing detection without relying on GPS. The technical approach involves building a multi-modal localization module and a cyber-resilient path planning system that can guide UAVS in GPS-denied or spoofed environments. These modules are evaluated on a multi-modal AI-enabled UAV research (MM-air) testbed, enabling validation under diverse operating conditions. The project contributes novel methods in sensor fusion, trajectory reconstruction, and secure autonomous navigation, with expected impact across the fields of cyber-physical systems, AI, and cybersecurity. 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.- subawards are not planned for this award.