摘 要：随着网络技术的快速发展，网络流量分类与异常检测成为网络安全领域的关键研究课题，传统方法在面对复杂多变的流量模式时逐渐显现出局限性。本研究基于深度学习技术，提出了一种融合卷积神经网络（CNN）和长短期记忆网络（LSTM）的混合模型，用于实现高精度的网络流量分类与异常检测。通过构建多层次特征提取机制，该模型能够自动挖掘流量数据中的时空关联特性，并有效应对流量模式的高度动态变化。实验采用真实世界的大规模流量数据集，结果表明，所提方法在分类准确率和异常检测召回率上均显著优于传统机器学习算法及单一深度学习模型。此外，研究还设计了一种自适应阈值调整策略，进一步提升了模型在不同网络环境下的鲁棒性。本研究的主要贡献在于提出了适用于复杂流量场景的高效深度学习框架，为智能化网络安全管理提供了新思路和技术支持。
关键词：网络流量分类；异常检测；深度学习
Abstract：With the rapid development of network technology, network traffic classification and anomaly detection have become key research topics in the field of network security. Traditional methods are increasingly showing limitations when dealing with complex and dynamic traffic patterns. This study proposes a hybrid model based on deep learning technology that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) for high-precision network traffic classification and anomaly detection. By constructing a multi-level feature extraction mechanism, the model is capable of automatically mining spatiotemporal correlation characteristics in traffic data and effectively addressing the highly dynamic changes in traffic patterns. Experiments were conducted using large-scale real-world traffic datasets, and the results demonstrate that the proposed method significantly outperforms traditional machine learning algorithms and single deep learning models in terms of classification accuracy and anomaly detection recall rate. Furthermore, this research designs an adaptive threshold adjustment strategy, which enhances the robustness of the model across different network environments. The primary contribution of this study lies in the proposal of an efficient deep learning fr amework tailored for complex traffic scenarios, providing new insights and technical support for intelligent network security management.
引言 1
一、深度学习与网络流量基础 1
(一)网络流量分类概述 1
(二)深度学习技术简介 2
(三)关键挑战与研究意义 2
二、数据预处理与特征提取方法 2
(一)网络流量数据获取 3
(二)特征选择与优化策略 3
(三)数据增强与标注技术 4
三、深度学习模型设计与应用 4
(一)常见深度学习模型分析 4
(二)模型架构设计与改进 5
(三)实验环境与参数设置 5
四、异常检测与性能评估研究 6
(一)异常检测算法设计 6
(二)性能指标体系构建 6
(三)实验结果与分析讨论 7
结论 7
参考文献 8
致谢 8
关键词：网络流量分类；异常检测；深度学习
Abstract：With the rapid development of network technology, network traffic classification and anomaly detection have become key research topics in the field of network security. Traditional methods are increasingly showing limitations when dealing with complex and dynamic traffic patterns. This study proposes a hybrid model based on deep learning technology that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) for high-precision network traffic classification and anomaly detection. By constructing a multi-level feature extraction mechanism, the model is capable of automatically mining spatiotemporal correlation characteristics in traffic data and effectively addressing the highly dynamic changes in traffic patterns. Experiments were conducted using large-scale real-world traffic datasets, and the results demonstrate that the proposed method significantly outperforms traditional machine learning algorithms and single deep learning models in terms of classification accuracy and anomaly detection recall rate. Furthermore, this research designs an adaptive threshold adjustment strategy, which enhances the robustness of the model across different network environments. The primary contribution of this study lies in the proposal of an efficient deep learning fr amework tailored for complex traffic scenarios, providing new insights and technical support for intelligent network security management.
Keywords: Network Traffic Classification；Anomaly Detection；Deep Learning
引言 1
一、深度学习与网络流量基础 1
(一)网络流量分类概述 1
(二)深度学习技术简介 2
(三)关键挑战与研究意义 2
二、数据预处理与特征提取方法 2
(一)网络流量数据获取 3
(二)特征选择与优化策略 3
(三)数据增强与标注技术 4
三、深度学习模型设计与应用 4
(一)常见深度学习模型分析 4
(二)模型架构设计与改进 5
(三)实验环境与参数设置 5
四、异常检测与性能评估研究 6
(一)异常检测算法设计 6
(二)性能指标体系构建 6
(三)实验结果与分析讨论 7
结论 7
参考文献 8
致谢 8
