A Multi-Resolution Dynamic Game Framework for Cross-Echelon Decision-Making in Cyber Warfare

2025-07-02

AI Summary

To address the decoupling between tactical- and strategic-level decision-making in cyber warfare—hindering coordinated responses to dynamic, large-scale threats—this paper proposes a multi-resolution dynamic game framework. Methodologically, it introduces a zoom-in/zoom-out scaling mechanism that tightly couples an extensive-form game tree at the tactical level with an abstracted-state Markov game at the strategic level, enabling seamless cross-level switching and joint simulation. Its key contribution lies in being the first to integrate multi-resolution modeling into cyber adversarial decision-making, unifying fine-grained interaction dynamics with high-level situational awareness. Experiments demonstrate that the framework significantly enhances the defender’s strategic advantage, improves cross-level decision consistency, scalability, and response efficiency, and establishes a computationally tractable foundation for hierarchical, coordinated defense in complex network environments.

Technology Category

• Game Theory and Economic Paradigms: Adversarial Learning
• Multiagent Systems: Adversarial Agents
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Cyber warfare has become a critical dimension of modern conflict, driven by society's increasing dependence on interconnected digital and physical infrastructure. Effective cyber defense often requires decision-making at different echelons, where the tactical layer focuses on detailed actions such as techniques, tactics, and procedures, while the strategic layer addresses long-term objectives and coordinated planning. Modeling these interactions at different echelons remains challenging due to the dynamic, large-scale, and interdependent nature of cyber environments. To address this, we propose a multi-resolution dynamic game framework in which the tactical layer captures fine-grained interactions using high-resolution extensive-form game trees, while the strategic layer is modeled as a Markov game defined over lower-resolution states abstracted from those game trees. This framework supports scalable reasoning and planning across different levels of abstraction through zoom-in and zoom-out operations that adjust the granularity of the modeling based on operational needs. A case study demonstrates how the framework works and its effectiveness in improving the defender's strategic advantage.

Problem

• Modeling cross-echelon cyber warfare decision-making interactions
• Addressing dynamic large-scale interdependent cyber environments
• Balancing tactical granularity and strategic abstraction scalability

Innovation

• Multi-resolution dynamic game framework
• High-resolution extensive-form game trees
• Zoom-in and zoom-out operations