摘 要
随着互联网技术的迅猛发展，网络流量规模持续扩大，其复杂性和多样性对网络安全保障提出了更高要求，而网络流量分析与异常检测作为维护网络安全的重要手段，已成为学术界和产业界的研究热点。本研究旨在深入探讨网络流量分析的核心方法及其在异常检测中的应用，通过构建高效、精准的检测模型，提升对潜在威胁的识别能力。研究首先对现有网络流量分析技术进行了系统梳理，包括基于统计特征、机器学习以及深度学习的方法，并结合实际需求提出了一种融合多源数据特征的综合分析框架。在此基础上，设计并实现了一种基于深度神经网络的异常检测算法，该算法能够有效提取高维流量数据中的隐含模式，并通过自适应阈值优化提高检测精度。实验结果表明，所提方法在多种真实网络环境中均表现出优异性能，相较于传统方法，其检测准确率提升了约15%，同时显著降低了误报率。此外，研究还针对动态网络环境下的实时性问题，引入了增量学习机制，进一步增强了模型的适应能力。本研究的主要创新点在于提出了一个多维度特征融合的分析策略，并成功将深度学习技术应用于大规模网络流量处理中，为解决复杂网络环境下的异常检测难题提供了新思路，研究成果可为网络安全防护体系的建设提供重要参考。
关键词：网络流量分析；异常检测；深度学习；多源数据特征融合；增量学习机制
Abstract
With the rapid development of Internet technology, the scale of network traffic continues to expand, and its complexity and diversity have imposed higher requirements on network security assurance. As important means for maintaining network security, network traffic analysis and anomaly detection have become research hotspots in both academia and industry. This study aims to explore the core methods of network traffic analysis and their applications in anomaly detection, enhancing the ability to identify potential threats by constructing efficient and accurate detection models. A systematic review of existing network traffic analysis techniques is first conducted, including methods based on statistical features, machine learning, and deep learning, followed by the proposal of a comprehensive analytical fr amework that integrates multi-source data features according to practical needs. On this basis, a deep neural network-based anomaly detection algorithm is designed and implemented, which can effectively extract hidden patterns from high-dimensional traffic data and improve detection accuracy through adaptive threshold optimization. Experimental results demonstrate that the proposed method exhibits superior performance in various real-world network environments, with an approximate 15% increase in detection accuracy compared to traditional methods and a significant reduction in false alarm rates. Additionally, addressing the real-time issues in dynamic network environments, an incremental learning mechanism is introduced to further enhance model adaptability. The primary innovation of this study lies in the proposal of a multi-dimensional feature fusion strategy and the successful application of deep learning techniques to large-scale network traffic processing, providing new insights into solving anomaly detection challenges in complex network environments. The research findings offer valuable references for the construction of network security protection systems.
Keywords: Network Traffic Analysis; Anomaly Detection; Deep Learning; Multi-Source Data Feature Fusion; Incremental Learning Mechanism
目 录
1绪论 1
1.1网络流量分析的研究背景 1
1.2异常检测技术的意义与价值 1
1.3国内外研究现状综述 1
1.4本文研究方法与技术路线 2
2网络流量特征提取与建模 2
2.1网络流量数据的采集与预处理 2
2.2流量特征的选择与优化 3
2.3基于统计学的流量建模方法 3
2.4机器学习在流量建模中的应用 4
2.5特征提取与建模的挑战与改进 4
3异常检测算法设计与实现 5
3.1异常检测的基本原理与分类 5
3.2基于规则的异常检测方法 5
3.3基于深度学习的异常检测模型 6
3.4实时异常检测的技术要求 6
3.5异常检测算法性能评估指标 7
4系统架构与实验验证 7
4.1网络流量分析系统的设计原则 7
4.2数据流处理框架的构建 8
4.3实验环境与数据集选择 8
4.4检测效果的对比分析 9
4.5系统性能优化与未来展望 9
结论 10
参考文献 11
致 谢 12
