Research Article

Finding Bayesian Nash equilibrium in DHR

¹ School of Data Science, Fudan University, Shanghai, 200437, China
² School of Computer Science, Fudan University, Shanghai, 200433, China
³ Institute of BigData, Fudan University, Shanghai, 200437, China
⁴ Purple Mountain Laboratories, Nanjing, 211111, China

^* Corresponding author (email: pchen@fudan.edu.cn)

Received: 11 May 2024
Revised: 18 November 2025
Accepted: 19 December 2024

Abstract

This study presents an effective cybersecurity defense mechanism by integrating the Dynamic Heterogeneous Redundancy (DHR) architecture with advanced algorithmic strategies to combat complex, evolving cyber threats while maintaining system performance and cost efficiency. Experimental results demonstrate that, particularly through Bayesian model-based attack-defense simulations, our scheduling strategy adapts swiftly to dynamically changing attack-defense environments. It effectively selects combinations of heterogeneous executors that meet both performance and economic requirements, thereby achieving a Bayesian Nash equilibrium. The findings not only validate our research hypothesis and methodology but also offer new perspectives and tools for advancing future cybersecurity defenses. This contribution enhances the theoretical foundation for constructing endogenous security intelligence methods and provides practical solutions, marking a significant advancement in cybersecurity, particularly in the application of dynamic heterogeneous redundancy technologies.