D3IR: Securing Multi-Domain Networks via Extending Depth-in-Defense Strategies Across Nested Management Domains

In an increasingly interconnected world, multi-domain networks serve as vital infrastructure, enabling seamless communication and resource sharing across diverse sectors, but also leading to increasingly frequent cyberattacks. Defense-in-depth (DiD) is widely regarded as a necessary strategy for mitigating these threats through layered security measures. However, current DiD strategies often fall short due to their single-domain focus, reliance on centralized control, and inability to adapt to dynamic threats. This paper proposes a novel framework to extend DiD strategies for multi-domain networks. It progressively defines key elements of multi-domain networks, culminating in a detailed hierarchical framework that clarifies the roles and interactions of management domains. Furthermore, cross-domain intrusion response is modeled as a multi-agent stochastic game, accounting for self-interested behavior and interactions between domains. The Independent Q-Learning (IQL) algorithm is employed to solve this game, with experimental results demonstrating substantial improvements in security across multi-domain environments.