Research on intelligent network traffic classification and dynamic scheduling mechanism based on deep learning
Ming Tang
Communication University of China, Nanjing
Wenting Ding
Communication University of China, Nanjing
Hongxiu Duan
Communication University of China, Nanjing
DOI: https://doi.org/10.59429/esta.v13i2.14560
Keywords: deep learning; network traffic classification; attention mechanism; dynamic scheduling; feature extraction
Abstract
The explosive growth of network traffic and increasing complexity of service types have rendered traditional classification and scheduling methods inadequate for meeting efficient transmission demands. By leveraging convolutional neural networks and long short-term memory networks as core architectures, combined with attention mechanisms, this study proposes an intelligent traffic classification model that automatically extracts spatiotemporal traffic features, enabling precise identification of encrypted and complex traffic patterns. A dynamic scheduling mechanism is designed based on classification results to optimize resource allocation and transmission efficiency. Experimental results demonstrate that this approach significantly improves classification accuracy and network throughput, providing robust technical support for intelligent network management and security protection.
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