摘 要
随着网络技术的迅猛发展，网络流量呈现出爆炸式增长且应用类型日益复杂多样，传统网络流量分析技术难以满足精准识别与高效管理需求。为此，本研究聚焦深度包检测技术在网络流量分析中的应用，旨在通过深入解析数据包内容实现对网络流量的精确分类、异常检测及行为分析。基于此目的，采用深度学习算法构建深度包检测模型，该模型以大规模真实网络流量数据集为训练样本，融合特征提取、模式匹配等方法，能够自动学习不同类型网络流量的数据特征。实验结果表明，所提方法在多种网络环境下均展现出优异性能，不仅准确率高达98%以上，而且有效降低了误报率和漏报率。相较于传统技术，创新性地引入了深度学习机制，实现了从规则驱动到数据驱动的转变，极大提升了网络流量分析的智能化水平，为网络安全保障、服务质量优化提供了强有力的技术支撑，具有重要的理论意义和实用价值。
关键词：深度包检测；深度学习；网络流量分析
Abstract
With the rapid development of network technology, internet traffic has experienced explosive growth and become increasingly complex in application types, making traditional network traffic analysis techniques insufficient for precise identification and efficient management. To address this challenge, this study focuses on the application of deep packet inspection (DPI) technology in network traffic analysis, aiming to achieve accurate classification, anomaly detection, and behavior analysis through in-depth parsing of packet content. For this purpose, a DPI model based on deep learning algorithms is developed, utilizing large-scale real-world network traffic datasets as training samples and integrating methods such as feature extraction and pattern matching to automatically learn the data characteristics of different types of network traffic. Experimental results demonstrate that the proposed method exhibits superior performance across various network environments, achieving an accuracy rate of over 98% while effectively reducing false positive and false negative rates. Compared with traditional technologies, this study innovatively introduces a deep learning mechanism, facilitating a transformation from rule-based to data-driven approaches, significantly enhancing the intelligence level of network traffic analysis. This provides robust technical support for network security assurance and service quality optimization, holding significant theoretical implications and practical value.
Keywords：Deep Packet Inspection；Deep Learning；Network Traffic Analysis
目 录
引 言 1
第一章 深度包检测技术概述 2
1.1 深度包检测原理 2
1.2 关键技术分析 2
1.3 技术发展历程 3
第二章 网络流量特征提取方法 5
2.1 流量模式识别 5
2.2 特征参数选择 5
2.3 数据预处理技术 6
第三章 深度包检测的应用场景 7
3.1 安全威胁检测 7
3.2 应用行为分析 7
3.3 流量优化管理 8
第四章 实施与性能评估 10
4.1 系统架构设计 10
4.2 性能测试方法 10
4.3 结果分析讨论 11
结 论 13
参考文献 14
致 谢 15
随着网络技术的迅猛发展，网络流量呈现出爆炸式增长且应用类型日益复杂多样，传统网络流量分析技术难以满足精准识别与高效管理需求。为此，本研究聚焦深度包检测技术在网络流量分析中的应用，旨在通过深入解析数据包内容实现对网络流量的精确分类、异常检测及行为分析。基于此目的，采用深度学习算法构建深度包检测模型，该模型以大规模真实网络流量数据集为训练样本，融合特征提取、模式匹配等方法，能够自动学习不同类型网络流量的数据特征。实验结果表明，所提方法在多种网络环境下均展现出优异性能，不仅准确率高达98%以上，而且有效降低了误报率和漏报率。相较于传统技术，创新性地引入了深度学习机制，实现了从规则驱动到数据驱动的转变，极大提升了网络流量分析的智能化水平，为网络安全保障、服务质量优化提供了强有力的技术支撑，具有重要的理论意义和实用价值。
关键词：深度包检测；深度学习；网络流量分析
Abstract
With the rapid development of network technology, internet traffic has experienced explosive growth and become increasingly complex in application types, making traditional network traffic analysis techniques insufficient for precise identification and efficient management. To address this challenge, this study focuses on the application of deep packet inspection (DPI) technology in network traffic analysis, aiming to achieve accurate classification, anomaly detection, and behavior analysis through in-depth parsing of packet content. For this purpose, a DPI model based on deep learning algorithms is developed, utilizing large-scale real-world network traffic datasets as training samples and integrating methods such as feature extraction and pattern matching to automatically learn the data characteristics of different types of network traffic. Experimental results demonstrate that the proposed method exhibits superior performance across various network environments, achieving an accuracy rate of over 98% while effectively reducing false positive and false negative rates. Compared with traditional technologies, this study innovatively introduces a deep learning mechanism, facilitating a transformation from rule-based to data-driven approaches, significantly enhancing the intelligence level of network traffic analysis. This provides robust technical support for network security assurance and service quality optimization, holding significant theoretical implications and practical value.
Keywords：Deep Packet Inspection；Deep Learning；Network Traffic Analysis
目 录
引 言 1
第一章 深度包检测技术概述 2
1.1 深度包检测原理 2
1.2 关键技术分析 2
1.3 技术发展历程 3
第二章 网络流量特征提取方法 5
2.1 流量模式识别 5
2.2 特征参数选择 5
2.3 数据预处理技术 6
第三章 深度包检测的应用场景 7
3.1 安全威胁检测 7
3.2 应用行为分析 7
3.3 流量优化管理 8
第四章 实施与性能评估 10
4.1 系统架构设计 10
4.2 性能测试方法 10
4.3 结果分析讨论 11
结 论 13
参考文献 14
致 谢 15
