Non-Equilibrium Game-Theoretic Learning Techniques For Mitigating Adversaries in Complex Adaptive Systems (CAS)
Abstract
The objective of this project is to develop novel learning frameworks for non-equilibrium game-theoretic adaptive algorithms for systems in adversarial, dynamic, and uncertain environments. The technical approach is to be inspired by interdisciplinary ideas from different fields, such as game theory, nonlinear adaptive learning theory, cognitive science and network security. The anticipated outcomes include the development of: (i) level-k learning architectures for security, where decision makers (defenders and attackers) are not perfectly rational and do not possess infinite intelligence; (ii) a learning-based moving target defense that reduces predictability for the attacker; and (iii) a resilient learning framework to bandwidth depletion attacks. The contributions and impact on ArmyÕs objectives will be the ability to multiply engagement capability, and enable coordination of distributed, heterogeneous teams of manned, unmanned vehicles and humans in dynamic, adversarial and completely uncertain environments. The models, theories, techniques, and tools developed as part of the proposed research will lay the foundation for the DoD to develop more effective techniques against intelligent attackers with different levels of rationality and capabilities. We intend to work with the US Army Research Lab(ARL), and industrial partners to transfer the proposed technology.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 22, 2019
- Source ID
- W911NF1910270
Entities
People
- Kyriakos G Vamvoudakis
Organizations
- Army Contracting Command
- Georgia Tech Research Corporation
- United States Army