In recent years, the evolution of cyber-attacks has exposed critical vulnerabilities in conventional defense mechanisms, particularly across national infrastructure systems such as power, transportation, and finance. Attackers are increasingly deploying persistent and sophisticated techniques to exfiltrate or manipulate sensitive data, surpassing static defense methods that depend on known vulnerabilities. This growing threat landscape underscores the urgent need for more advanced and adaptive defensive strategies to counter continuously evolving attack vectors. To address this challenge, this paper proposes a novel reinforcement learning-based optimization framework integrated with a Dynamic Heterogeneous Redundancy (DHR) architecture. Our approach uniquely utilizes reinforcement learning for the dynamic scheduling of encryption-layer configurations within the DHR framework, enabling adaptive adjustment of defense policies based on system status and threat progression. We evaluate the proposed system in a simulated adversarial environment, where reinforcement learning continuously adjusts encryption strategies and defense behaviors in response to evolving attack patterns and operational dynamics. Experimental results demonstrate that our method achieves a higher defense success rate while maintaining lower defense costs, thereby enhancing system resilience against cyber threats and improving the efficiency of defensive resource allocation.
Yu et al. (Tue,) studied this question.
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