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Completed COOPERATIVE AGREEMENT National Science Foundation (US)

NSF Convergence Accelerator Track G: AVOID 5G: Automated Verification Of Internet Data-paths for 5G

$50M USD

Funder National Science Foundation (US)
Recipient Organization Johns Hopkins University
Country United States
Start Date Sep 01, 2023
End Date Aug 31, 2025
Duration 730 days
Number of Grantees 5
Roles Principal Investigator; Co-Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2326928
Grant Description

Department of Defense (DOD) use of commercial 5th Generation (5G) networks entails unprecedented reliance on untrusted third-party communications infrastructure, including the 5G base stations that connect directly to 5G devices and the Internet infrastructure that underlies 5G communications. The core problem when operating through non-cooperative commercial 5G infrastructure is that the unknown infrastructure potentially exposes communications to an adversary.

Traversing adversary-controlled infrastructure allows DOD’s adversaries to recognize, disrupt, or extract intelligence even from encrypted communications. Increasingly complex obfuscation techniques have created an arms race against network intelligence techniques to detect the obfuscation. With each new obfuscation, DOD can never know if it fools the adversary, or if the adversary is simply lulling DOD into a false sense of security.The next great capability leap for operating through 5G networks will likely come from sophisticated analytics that provide situational awareness of threats within the communications infrastructure, and an implementation that dynamically routes communications along benign paths.

Through a team that combines expertise across disciplines and sectors, this project will pursue this transformative capability and accelerate convergence on a new DOD 5G defense: restructuring communication paths to avoid adversary-controlled base stations, networks, and locations, thereby keeping DOD communications unobservable by the adversary.

This project proposes a system—Automated Verification Of Internet Data-paths (AVOID)—that creates this unprecedented capability through two deliverables that tackle two high risk attack vectors for 5G communications. Deliverable 1 targets the potential for adversaries to control commercial base stations anywhere in the world, allowing them to subvert DOD communications at the point of entry intro the wireless network.

AVOID will recognize malicious and surveillance base stations, and provide a mechanism for DOD devices to connect to specific benign base stations. Deliverable 2 combats the ability of our adversaries to apply sophisticated and complex network analytics to any DOD communications that traverse networks or territory they control. AVOID will embed topologic and geographic awareness into a routing system, and provide a mechanism for DOD’s communications to avoid adversary-controlled territory across the global Internet and provide safe paths to DOD-controlled networks.

Combined, these deliverables will provide end-to-end adversary avoidance without requiring modification to existing applications or routers in DOD networks, or cooperation by any third-party network.The intellectual merit comes from addressing a persistently unsolved national security challenge which requires convergence across seven areas of expertise: 5G communication and network management; Internet path analytics; overlay routing; operational network security; test and evaluation that targets DOD scenarios; technology transition; and professional project management. AVOID 5G will also broadly impact society.

The deliverables complement the broader landscape of zero-trust architectures, secure 5G implementations, and ongoing obfuscation technique development. Government partners included in the team provide a channel for research and education outcomes, including cybersecurity workforce training.

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.

All Grantees

Johns Hopkins University

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