Design of Adaptive Network Defense Mechanism Combining Reinforcement Learning

In the context of increasingly severe cybersecurity threats, traditional defense mechanisms struggle against dynamic and unknown attacks. This study proposes an adaptive network defense mechanism integrating a Deep Q-Network (DQN) with RL. The DQN model processes real-time network traffic features to detect attacks via Q-value updates, while RL dynamically optimizes defense strategies through environmental interaction and reward mechanisms. Experiments simulated diverse attacks in a virtual enterprise network. Results demonstrated high detection accuracy (average F1-score >0.90), rapid response (≤2 seconds), and defense success rates of 89–93%. Notably, the system achieved adaptability indices of 0.82 –0.88 against unknown attacks, outperforming static methods. The mechanism’s self-learning capability enables real-time adjustments to emerging threats, significantly enhancing defense flexibility. This work advances intelligent cybersecurity by merging deep learning with RL, offering a robust solution for dynamic network environments. Its success in balancing computational efficiency and defense efficacy highlights practical applicability in high-security sectors like finance and enterprise networks.