Year: 2024
Type: article
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.
Source: Security and Safety
Authors: Yue Wu, Yu Liu, Ping Chen
Institutions: Fudan University, Purple Mountain Laboratories
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405599195