Will the drop zone stay clear?
Overhead drone video of a delivery pad. GWU’s model tracks the people and objects around it, forecasts how long the zone will stay clear, and decides whether to lower the package, wait, or treat the zone as occupied.
NASA University Leadership Initiative
SkySec: Securing the skies over our cities.
Drones and air taxis will soon share dense urban airspace, relying on autonomy software, aircraft sensors, automated airspace management, and communication, navigation, and data links, all of which can be attacked. Eight institutions are working together to find those weaknesses, defend against them, and mitigate the risks.
Animated illustration: drone traffic over a city. One aircraft broadcasts a false position; the system detects the mismatch, marks an unsafe region around its true location, and nearby traffic routes around it.
The challenge
Thousands of autonomous aircraft. One shared sky. Every link a potential attack surface.
Advanced Air Mobility will bring electric air taxis (eVTOLs) and fleets of small drones into the airspace above our cities, coordinated by automated traffic-management services rather than human controllers alone. That autonomy depends on perception, navigation, broadcast surveillance, and data links that an adversary can spoof, jam, or manipulate.
This project is building the security framework and algorithmic foundations to keep high-density urban airspace safe, even in the presence of malicious activity, in support of NASA’s Advanced Air Mobility and UAS Traffic Management (UTM) research.
- Partner institutions
- 8
- Publications
- 36
- Team members
- 48
- Technical challenges
- 3
Research framework
Three technical challenges, one integrated approach
Each challenge feeds the next: attacks we identify become the threats we defend against, and the defenses that work shape the architecture we harden.
TC1
Identify
Identify vulnerabilities and model attack impact
Find the weak links in aircraft autonomy and in UTM/AAM architectures, quantify the risk of attacks, and detect them with machine learning.
- 1.1Identify attacks and vulnerabilities in aircraft autonomy and traffic management architectures
- 1.2Model attack impacts and quantify attack risks
- 1.3Monitor and detect attacks with machine learning
TC2
Defend
Defend aircraft and the airspace
Mitigate attack hazards before and during flight, for an individual aircraft and for many aircraft sharing the same urban airspace.
- 2.1Manage security risks in flight planning with belief sharing
- 2.2Mitigate attack hazards during flight at the individual-aircraft level
- 2.3Mitigate attack hazards for multiple aircraft at the airspace level
TC3
Harden
Design resilient, secure infrastructure
Harden system architectures with security properties, assure those properties formally, and robustify data flow between aircraft and services.
- 3.1Harden the system architecture with security properties
- 3.2Assure the security properties of the integrated architecture
- 3.3Robustify communication between aircraft and traffic management services
Cross-cutting
Validation and demonstration: simulation testing, hardware testbeds, and integrated live-virtual-constructive demonstrations, with Year 3 focused on aviation use-case demos.
Research overviewAviation use cases
Research grounded in real operations
Cross-institution teams have formed around concrete scenarios, from package delivery to air-taxi landings, each pairing a credible threat with a defense.
- CaseUC-01Mission
Vision-Based Delivery and Landing
Making camera- and AI-based package delivery and precision landing trustworthy, even when the scene below can be manipulated.
ThreatPhysical adversarial objects, tampered landing markers, and injected visual text that mislead onboard perception.
Teams- GWU
- UC Irvine
- TSU
TC- TC1
- TC2
- CaseUC-02Mission
Separation Assurance in Shared Airspace
Keeping drones from multiple operators safely apart when fleets, policies, and navigation data differ, or are corrupted.
ThreatGPS/GNSS degradation and spoofing, and unsafe interactions among heterogeneous fleets and collision-avoidance policies.
Teams- GWU
- Purdue
- UT Austin
TC- TC2
- CaseUC-03Mission
Trustworthy Remote ID
Detecting falsified Remote ID broadcasts from the physics of the radio signal, and planning safe paths around suspected spoofers.
ThreatAn aircraft broadcasting false position and velocity to hide its intent or manipulate nearby aircraft.
Teams- Vanderbilt
- UT Austin
TC- TC1
- TC2
- CaseUC-04Mission
Air Traffic Control Voice Integrity
Assessing how convincingly AI speech synthesis can imitate controller instructions, to inform procedural and technical defenses.
ThreatSynthetic controller voices delivering unauthorized instructions over voice radio.
Teams- GWU
TC- TC1
- CaseUC-05Mission
Command-and-Control Link Security
Evaluating and hardening the MAVLink link between ground control stations and drones, and detecting hijacked vehicles.
ThreatInterception, command injection, and telemetry spoofing on unauthenticated control links.
Teams- NVCC
- Vanderbilt
- GWU
- Collins
TC- TC1
- TC3
- CaseUC-06Mission
Resilient eVTOL Emergency Landing
Selecting and tracking a safe landing zone with onboard sensing when an air taxi is impaired by a cyber-physical attack.
ThreatAttacks that degrade navigation or flight capability over a dense urban environment.
Teams- TSU
TC- TC2
- CaseUC-07Mission
Secure and Fair Airspace Coordination
Coordinating flight plans, sequencing, and resources among self-interested operators without a single point of failure.
ThreatCompromised coordinators, strategic misreporting, collusion, and spoofed arrival information.
Teams- Purdue
- UT Austin
- Vanderbilt
TC- TC2
- TC3
- CaseUC-08Mission
Zero-Trust and Formally Assured Architecture
Building drone software architectures with zero-trust patterns, and proving whether attacks are possible with a theorem prover.
ThreatVulnerabilities that propagate between untrusted components of drone and ground-station software.
Teams- Collins
- Vanderbilt
TC- TC3
The team
Eight institutions, coast to coast
Universities, an industry partner, and a community college bring together expertise in aviation, autonomy, cybersecurity, control, and machine learning.
- GWUThe George Washington UniversityWashington, DC
- VanderbiltVanderbilt UniversityNashville, TN
- PurduePurdue UniversityWest Lafayette, IN
- UT AustinThe University of Texas at AustinAustin, TX
- UC IrvineUniversity of California, IrvineIrvine, CA
- TSUTennessee State UniversityNashville, TN
- CollinsCollins AerospaceCedar Rapids, IA
- NVCCNorthern Virginia Community CollegeAnnandale, VA
Publications
Selected publications
IEEE Transactions on Automatic Control · 2026
Noncooperative Coordination via a Trading-Based Auction
UT Austin
AIAA SciTech Forum · 2026
A Survey of Security Challenges and Solutions for UAS Traffic Management (UTM) and Small Unmanned Aerial Systems (sUAS)
GWU · Purdue · Vanderbilt
AIAA/IEEE Digital Avionics Systems Conference · 2026
Remote ID Spoofing-Aware Trajectory Planning for Small Unmanned Aerial Systems
Vanderbilt · UT Austin
Network and Distributed System Security Symposium · 2026
FlyTrap: Physical Distance-Pulling Attack Towards Camera-based Autonomous Target Tracking Systems
UC Irvine
Where we are
From algorithms to demonstrations
Two years in, the project is moving its research into integrated demonstrations that industry, government, and the public can see working.
DemosYear 1 · 2024–25
Foundations
Threat surveys, simulation platforms, and the first attacks and defenses.
Year 2 · 2025–26
Aviation use cases
Cross-team use cases, hardware testbeds, and published results.
Year 3 · 2026–27Now
Demonstrations
Integrated simulation and flight-test demos, and a project Demo Day in Spring 2027.
Tracking a safe landing zone during descent
TSU’s emergency landing system locks onto the selected landing zone and keeps it centered as the aircraft descends, with live tracking plots.
FlyTrap: attacking autonomous target tracking
UC Irvine’s demonstration of FlyTrap, the first security analysis of camera-based autonomous target tracking in drones. A specially patterned umbrella draws a tracking drone dangerously close. Evaluated on commercial drones and presented at NDSS 2026.
Networked UAV simulation over a city
Vanderbilt’s networked simulator flies many drones from multiple operators over a downtown map, each running its own ArduPilot autopilot. It’s the platform used to generate attack datasets and evaluate detectors.
News & events
Latest from the project
Four papers at DASC and ITSC 2026
Results on GPS-robust separation assurance, spoofing-aware trajectory planning, vision-language safe-zone detection, and heterogeneous fleets appeared at the Digital Avionics Systems Conference and the IEEE ITSC.
Read more: Four papers at DASC and ITSC 2026Year 2 annual review at Purdue
The team met at Purdue University to present Year 2 results, including student posters from NVCC interns, and to plan Year 3 demonstrations.
Second NVCC intern cohort builds the GridEye UTM monitor
Four NVCC interns studied MAVLink control-link security and built GridEye, a UTM monitoring dashboard, then presented posters at the Year 2 review.
Read more: Second NVCC intern cohort builds the GridEye UTM monitor
Work with us
Help shape secure urban air mobility.
We welcome conversations with industry, government agencies, and fellow researchers: feedback on our use cases, operational perspective, and partnership on demonstrations.
Get in touch