Meta Asynchronous Advantage Actor-Critic Adaptive Dynamic Defense Model Based on Adversarial Training

In view of the increasingly complex challenges of current network attacks, and the significant deficiencies of existing defense mechanisms in real-time adjustment of defense strategies, dynamic adaptability and robustness, this paper proposes a meta asynchronous advantage actor critic adaptive dynamic defense model based on meta learning and adversarial training. Firstly, a multi-threaded parallel training mechanism is designed based on the asynchronous advantage actor-critic(A3C). Through asynchronous sampling and advantage function estimation, the training efficiency and stability are improved, and the efficient strategy search and optimal defense decision are realized. Then, the projected gradient descent (PGD) algorithm is used to generate countermeasure samples and introduce the countermeasure training process to enhance the robustness of the model and ensure that the system can still maintain high detection accuracy and decision reliability in complex environments. Finally, the model-agnostic meta-learning (MAML) algorithm is used to construct the meta optimization layer, so that the model can quickly learn new attack characteristics from a small number of samples, realize the efficient adaptation to unknown attack tasks, and significantly improve the response and recognition ability to zero day attacks and new threats. The experimental results show that the model has excellent defense performance, fast adaptability and strong anti-interference characteristics on multiple intrusion detection data sets, and provides a reliable technical path for the construction of the next generation of intelligent, active and continuously evolving dynamic network security defense system.