Exploiting self-evolutionary strategies of components for Dynamic Heterogeneous Redundancy

The network security situation is increasingly serious. Traditional security equipment like firewalls and intrusion detection systems cannot prevent unknown risks due to their passive nature. Dynamic Heterogeneous Redundancy (DHR) actively defends by switching the attack surface and confusing attackers, becoming essential in modern network defense. However, existing DHR approaches based on scheduling algorithms struggle under high-intensity attacks due to resource limitations. To overcome this weakness, we propose a Genetic Algorithm and Particle Swarm Optimization (GAPSO), a novel DHR architecture. GAPSO provides real-time security awareness of host components, maps potential attacker paths, and calculates the risk probability of each component. High-risk components evolve into others in the pool during attacks. Experiments show that GAPSO significantly reduces system risk compared to scheduling-based DHR and effectively delays the hacker’s attack lifecycle. Additionally, we developed a prototype system and evaluated it in a real network environment, obtaining positive results.