摘 要
随着互联网的迅猛发展，网络流量呈现出复杂多变的特点，异常流量检测成为保障网络安全的关键技术。本研究旨在深入探讨网络流量特征提取与异常流量检测算法，以应对日益复杂的网络环境。通过对现有流量特征提取方法的分析，提出一种基于深度学习的多维度特征融合模型，该模型结合了统计特征、协议特征和行为特征，能够更全面地刻画网络流量的本质属性。在此基础上，设计了一种自适应异常检测算法，利用生成对抗网络（GAN）进行异常样本的生成与识别，有效解决了传统方法中异常样本不足的问题。实验结果表明，所提出的特征提取模型在多种网络环境下均能准确提取流量特征，且自适应异常检测算法相较于传统方法具有更高的检测率和更低的误报率。本研究的主要创新点在于构建了多维度特征融合模型，并引入GAN机制实现异常流量的高效检测，为网络流量监测提供了新的思路和技术手段，对提升网络安全防护能力具有重要意义。
关键词：网络流量特征提取；多维度特征融合模型；生成对抗网络
Abstract
With the rapid development of the Internet, network traffic has become increasingly complex and dynamic, making anomaly detection a critical technology for ensuring network security. This study aims to explore network traffic feature extraction and anomaly detection algorithms to address the growing complexity of network environments. By analyzing existing traffic feature extraction methods, we propose a multi-dimensional feature fusion model based on deep learning that integrates statistical features, protocol features, and behavioral features, providing a more comprehensive characterization of the intrinsic properties of network traffic. Building on this, we design an adaptive anomaly detection algorithm that utilizes Generative Adversarial Networks (GAN) for the generation and identification of anomaly samples, effectively addressing the issue of insufficient anomaly samples in traditional methods. Experimental results demonstrate that the proposed feature extraction model accurately captures traffic features across various network environments, and the adaptive anomaly detection algorithm exhibits higher detection rates and lower false alarm rates compared to conventional approaches. The primary innovations of this research lie in constructing a multi-dimensional feature fusion model and incorporating GAN mechanisms to achieve efficient anomaly traffic detection, offering new perspectives and technical means for network traffic monitoring, which is significant for enhancing network security protection capabilities.
Keywords：Network Traffic Feature Extraction；Multi-dimensional Feature Fusion Model；Generative Adversarial Network
目 录
引 言 1
第一章 网络流量特征分析基础 2
1.1 网络流量特征概述 2
1.2 特征提取技术综述 2
1.3 特征选择方法研究 3
第二章 异常流量检测模型构建 5
2.1 检测模型原理分析 5
2.2 常见算法性能对比 5
2.3 模型优化策略探讨 6
第三章 特征提取与检测算法融合 8
3.1 融合框架设计思路 8
3.2 关键技术实现路径 8
3.3 实验验证与结果分析 9
第四章 应用场景与未来展望 11
4.1 典型应用场景分析 11
4.2 面临挑战与应对措施 11
4.3 未来发展方向预测 12
结 论 14
参考文献 15
致 谢 16
随着互联网的迅猛发展，网络流量呈现出复杂多变的特点，异常流量检测成为保障网络安全的关键技术。本研究旨在深入探讨网络流量特征提取与异常流量检测算法，以应对日益复杂的网络环境。通过对现有流量特征提取方法的分析，提出一种基于深度学习的多维度特征融合模型，该模型结合了统计特征、协议特征和行为特征，能够更全面地刻画网络流量的本质属性。在此基础上，设计了一种自适应异常检测算法，利用生成对抗网络（GAN）进行异常样本的生成与识别，有效解决了传统方法中异常样本不足的问题。实验结果表明，所提出的特征提取模型在多种网络环境下均能准确提取流量特征，且自适应异常检测算法相较于传统方法具有更高的检测率和更低的误报率。本研究的主要创新点在于构建了多维度特征融合模型，并引入GAN机制实现异常流量的高效检测，为网络流量监测提供了新的思路和技术手段，对提升网络安全防护能力具有重要意义。
关键词：网络流量特征提取；多维度特征融合模型；生成对抗网络
Abstract
With the rapid development of the Internet, network traffic has become increasingly complex and dynamic, making anomaly detection a critical technology for ensuring network security. This study aims to explore network traffic feature extraction and anomaly detection algorithms to address the growing complexity of network environments. By analyzing existing traffic feature extraction methods, we propose a multi-dimensional feature fusion model based on deep learning that integrates statistical features, protocol features, and behavioral features, providing a more comprehensive characterization of the intrinsic properties of network traffic. Building on this, we design an adaptive anomaly detection algorithm that utilizes Generative Adversarial Networks (GAN) for the generation and identification of anomaly samples, effectively addressing the issue of insufficient anomaly samples in traditional methods. Experimental results demonstrate that the proposed feature extraction model accurately captures traffic features across various network environments, and the adaptive anomaly detection algorithm exhibits higher detection rates and lower false alarm rates compared to conventional approaches. The primary innovations of this research lie in constructing a multi-dimensional feature fusion model and incorporating GAN mechanisms to achieve efficient anomaly traffic detection, offering new perspectives and technical means for network traffic monitoring, which is significant for enhancing network security protection capabilities.
Keywords：Network Traffic Feature Extraction；Multi-dimensional Feature Fusion Model；Generative Adversarial Network
目 录
引 言 1
第一章 网络流量特征分析基础 2
1.1 网络流量特征概述 2
1.2 特征提取技术综述 2
1.3 特征选择方法研究 3
第二章 异常流量检测模型构建 5
2.1 检测模型原理分析 5
2.2 常见算法性能对比 5
2.3 模型优化策略探讨 6
第三章 特征提取与检测算法融合 8
3.1 融合框架设计思路 8
3.2 关键技术实现路径 8
3.3 实验验证与结果分析 9
第四章 应用场景与未来展望 11
4.1 典型应用场景分析 11
4.2 面临挑战与应对措施 11
4.3 未来发展方向预测 12
结 论 14
参考文献 15
致 谢 16
