Network attack security detection model based on model agnostic meta-learning algorithm

To address the evolving dynamics of network intrusion threats, this study proposes a Model-Agnostic Meta-learning (MAML)-based network attack detection model that utilizes an error-corrected few-shot dataset to enhance generalization across diverse attack scenarios. The model integrates an optimized Convolutional Neural Network (CNN) architecture with meta-learning algorithms, enabling adaptive feature extraction and rapid task-specific adaptation. By innovatively combining few-shot learning with CNN-based meta-learning, the framework achieves robust performance in detecting both simulated and real-world attacks. Empirical evaluations demonstrated that the model achieved near-optimal loss convergence (loss value close to 0) and exhibited an excellent training curve, indicating strong optimization stability. In real-world deployments, the model achieved a defense action probability of 0.85 for simulated attacks, a detection accuracy of 0.91, and a recognition accuracy of 0.92 for real-world attacks, outperforming baseline methods in dynamic network environments. These results highlight the model's effectiveness in improving network platform defenses against complex and evolving attack behaviors, thereby advancing the most advanced in adaptive intrusion detection systems.