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
Mar 07, 2023
Source ID
FA95502110239

Entities

People

  • Tahir Ekin

Organizations

  • Air Force Office of Scientific Research
  • Texas State University
  • United States Air Force

Tags

Fields of Study

  • Computer science

Readers

  • Enterprise Information Systems Architecture and Joint Command Capability Interoperability Support.
  • Neural Network Machine Learning.
  • Strategic Security Studies

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks
  • Fully Networked C3
  • Fully Networked C3 - Command and Control