Network Defense Strategy Model Based on Deep Reinforcement Learning and Evolutionary Game Theory

To address the limitations of deep reinforcement learning (DRL) in modeling adversarial strategy evolution and the rigidity of evolutionary game theory in dynamic scenarios, this paper proposes a Dual-layer Cooperative Evolutionary Framework (DCEF). DCEF integrates the Asynchronous Advantage Actor-Critic (A3C) algorithm for real-time defense generation with an evolutionary game layer for global optimization, leveraging an incremental payoff matrix and adaptive triggering mechanisms. Experiments on the CICIDS2017 dataset demonstrate DCEF’s superiority: 86.2% defense success rate (5.6% higher than A3C), 38ms response latency, and robust adaptability to multi-stage attacks. The framework effectively balances short-term responsiveness with long-term strategic stability.