Year: 2025
Type: preprint
Abstract: In this work, we model Moving Target Defence (MTD) as a partially observable stochastic game between an attacker and a defender. The attacker tries to compromise the system through probing actions, while the defender minimizes the risk by reimaging the system, balancing between performance cost and security level. We demonstrate that the optimal strategies for both players follow a threshold structure. Based on this insight, we propose a structure-aware policy gradient reinforcement learning algorithm that helps both players converge to the Nash equilibrium. This approach enhances the defender's ability to adapt and effectively counter evolving threats, improving the overall security of the system. Finally, we validate the proposed method through numerical simulations.
Source: arXiv (Cornell University)
Authors: Datar, Mandar, Dujardin, Yann
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7077973848
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