Hardening JADC2 to Adversarial Data: Theory, Algorithms and Applications
Abstract
All command and control (C2) decisions are founded upon information which can be manipulated by adversarial attacks. The emerging Joint All Domain Command and Control (JADC2) construct entails a highly networked infrastructure heavily reliant on machine learning for data fusion and synthesis. Direct manipulation of digital data (e.g., airborne sensor output, social media posts) can significantly alter the performance of such algorithms and thus constitutes a serious threat to an emerging friendly center of gravity. Therefore, we propose a novel Dynamic Data Driven Application Systems (DDDAS) framework.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 21, 2022
- Source ID
- FA95502110239XX0
Entities
People
- Tahir Ekin
Organizations
- Air Force Office of Scientific Research
- Texas State University
- United States Air Force