Research on Deep Learning-based Network Intrusion Detection Model

This paper investigates the deep learning-based network intrusion detection model to address the problem of recognizing unknown attacks. By analyzing existing models, the potential of applying deep learning in network intrusion detection is explored, with a particular focus on the capabilities of discriminative variational autoencoders and generative adversarial networks in extracting and recognizing network traffic and attack features. We construct a deep learning-based network intrusion detection model with the aim of improving the recognition rate of new attacks and reducing false positives. By evaluating and comparing performance metrics, the advantages of the proposed model in recognizing unknown attacks are validated. Experimental results demonstrate that the model achieves higher recognition rates and accuracy for unknown attacks, outperforming traditional models in intrusion detection performance. This research provides important theoretical and practical guidance for the development and application of network intrusion detection.