摘 要
随着信息技术的迅猛发展，网络安全威胁日益复杂多变，传统基于规则和特征匹配的入侵检测系统面临诸多挑战。为应对新型网络攻击，本研究聚焦于网络行为分析在入侵检测系统中的应用，旨在通过深入挖掘网络流量中的行为模式，构建更加智能、高效的入侵检测机制。研究采用数据驱动的方法，首先收集并预处理大规模真实网络流量数据，提取包括时间序列特征、协议特征及用户行为特征在内的多维度特征集。然后利用机器学习与深度学习算法建立分类模型，特别是引入图神经网络以捕捉节点间复杂的依赖关系。实验结果表明，所提出的基于网络行为分析的入侵检测方法能够有效识别已知和未知攻击类型，在准确率、召回率等关键性能指标上显著优于传统方法。
关键词：网络行为分析 入侵检测系统 机器学习
Abstract
With the rapid development of information technology, network security threats are increasingly complex and changeable, and the traditional intrusion detection system based on rules and feature matching faces many challenges. In order to deal with new network attacks, this study focuses on the application of network behavior analysis in intrusion detection system, aiming to build a more intelligent and efficient intrusion detection mechanism by deeply excavating the behavior mode in network traffic. The study adopts a data-driven approach, first collecting and preprocessing large-scale real network traffic data to extract multi-dimensional feature sets including time series features, protocol features and user behavior characteristics. Then machine learning and deep learning algorithms are used to build classification models, especially graph neural networks are introduced to capture the complex dependencies among nodes. The experimental results show that the proposed intrusion detection method based on network behavior analysis can effectively identify known and unknown attack types and significantly outperform traditional methods in key performance indicators such as accuracy and recall.
Keyword:Network Behavior Analysis Intrusion Detection System Machine Learning
目 录
1绪论 1
1.1网络行为分析的研究背景与意义 1
1.2入侵检测系统的发展现状综述 1
1.3本文研究方法与技术路线 2
2网络行为特征提取与建模 2
2.1行为特征的定义与分类 2
2.2特征提取算法的选择与优化 3
2.3行为模型的构建与评估 3
3基于网络行为的异常检测技术 4
3.1异常行为模式识别方法 4
3.2实时监测与动态调整机制 5
3.3检测准确率提升策略研究 5
4网络行为分析的应用实践 6
4.1在企业网络安全中的应用 6
4.2面向云计算环境的入侵检测 6
4.3新型攻击场景下的应对措施 7
结论 8
参考文献 9
致谢 10
随着信息技术的迅猛发展，网络安全威胁日益复杂多变，传统基于规则和特征匹配的入侵检测系统面临诸多挑战。为应对新型网络攻击，本研究聚焦于网络行为分析在入侵检测系统中的应用，旨在通过深入挖掘网络流量中的行为模式，构建更加智能、高效的入侵检测机制。研究采用数据驱动的方法，首先收集并预处理大规模真实网络流量数据，提取包括时间序列特征、协议特征及用户行为特征在内的多维度特征集。然后利用机器学习与深度学习算法建立分类模型，特别是引入图神经网络以捕捉节点间复杂的依赖关系。实验结果表明，所提出的基于网络行为分析的入侵检测方法能够有效识别已知和未知攻击类型，在准确率、召回率等关键性能指标上显著优于传统方法。
关键词：网络行为分析 入侵检测系统 机器学习
Abstract
With the rapid development of information technology, network security threats are increasingly complex and changeable, and the traditional intrusion detection system based on rules and feature matching faces many challenges. In order to deal with new network attacks, this study focuses on the application of network behavior analysis in intrusion detection system, aiming to build a more intelligent and efficient intrusion detection mechanism by deeply excavating the behavior mode in network traffic. The study adopts a data-driven approach, first collecting and preprocessing large-scale real network traffic data to extract multi-dimensional feature sets including time series features, protocol features and user behavior characteristics. Then machine learning and deep learning algorithms are used to build classification models, especially graph neural networks are introduced to capture the complex dependencies among nodes. The experimental results show that the proposed intrusion detection method based on network behavior analysis can effectively identify known and unknown attack types and significantly outperform traditional methods in key performance indicators such as accuracy and recall.
Keyword:Network Behavior Analysis Intrusion Detection System Machine Learning
目 录
1绪论 1
1.1网络行为分析的研究背景与意义 1
1.2入侵检测系统的发展现状综述 1
1.3本文研究方法与技术路线 2
2网络行为特征提取与建模 2
2.1行为特征的定义与分类 2
2.2特征提取算法的选择与优化 3
2.3行为模型的构建与评估 3
3基于网络行为的异常检测技术 4
3.1异常行为模式识别方法 4
3.2实时监测与动态调整机制 5
3.3检测准确率提升策略研究 5
4网络行为分析的应用实践 6
4.1在企业网络安全中的应用 6
4.2面向云计算环境的入侵检测 6
4.3新型攻击场景下的应对措施 7
结论 8
参考文献 9
致谢 10
