摘 要：随着网络攻击手段的日益复杂化和隐蔽化，精准溯源成为网络安全领域的关键挑战之一。本研究聚焦于网络攻击溯源中的行为分析与特征提取技术，旨在通过深入解析攻击行为模式，提升攻击溯源的准确性和效率。研究结合机器学习算法与大数据分析方法，提出了一种基于多层次行为特征提取的新型溯源框架，该框架能够从海量网络流量中自动识别并提取攻击者的独特行为特征。实验结果表明，所提出的框架在复杂网络环境中展现出较高的检测精度和较低的误报率，尤其在APT（高级持续性威胁）攻击场景下表现优异。此外，本研究创新性地引入了时间序列分析以捕捉动态攻击行为，并通过多维度特征融合增强了模型的鲁棒性。总体而言，本研究为网络攻击溯源提供了新的技术路径，显著提升了对未知攻击的识别能力，为构建更加安全的网络环境奠定了理论和技术基础。
关键词：网络攻击溯源；行为特征提取；机器学习
Abstract：With the increasing complexity and concealment of cyber-attack methods, precise attribution has become one of the key challenges in the field of cybersecurity. This study focuses on behavioral analysis and feature extraction techniques for cyber-attack attribution, aiming to enhance the accuracy and efficiency of attack tracing through an in-depth examination of attack behavior patterns. By integrating machine learning algorithms with big data analytics, a novel attribution fr amework based on multi-level behavioral feature extraction is proposed, which can automatically identify and extract unique behavioral characteristics of attackers from massive network traffic. Experimental results demonstrate that the proposed fr amework exhibits high detection accuracy and low false-positive rates in complex network environments, particularly excelling in APT (Advanced Persistent Threat) attack scenarios. Additionally, this study innovatively incorporates time-series analysis to capture dynamic attack behaviors and enhances model robustness through multi-dimensional feature fusion. Overall, this research provides new technical approaches for cyber-attack attribution, significantly improving the capability to identify unknown attacks and laying a theoretical and technical foundation for constructing a more secure network environment.
引言 1
一、网络攻击溯源基础理论 1
(一)攻击溯源的概念与意义 1
(二)行为分析的基本原理 2
(三)特征提取的核心方法 2
二、行为分析技术在溯源中的应用 3
(一)数据采集与预处理技术 3
(二)异常行为检测模型构建 3
(三)行为模式的关联分析 4
三、特征提取的关键技术研究 4
(一)特征选择与降维方法 4
(二)动态特征提取算法设计 5
(三)特征表示与优化策略 5
四、溯源技术的实际应用与挑战 6
(一)实验环境与数据集构建 6
(二)溯源系统的性能评估 6
(三)当前技术面临的挑战 7
结论 7
参考文献 9
致谢 9
关键词：网络攻击溯源；行为特征提取；机器学习
Abstract：With the increasing complexity and concealment of cyber-attack methods, precise attribution has become one of the key challenges in the field of cybersecurity. This study focuses on behavioral analysis and feature extraction techniques for cyber-attack attribution, aiming to enhance the accuracy and efficiency of attack tracing through an in-depth examination of attack behavior patterns. By integrating machine learning algorithms with big data analytics, a novel attribution fr amework based on multi-level behavioral feature extraction is proposed, which can automatically identify and extract unique behavioral characteristics of attackers from massive network traffic. Experimental results demonstrate that the proposed fr amework exhibits high detection accuracy and low false-positive rates in complex network environments, particularly excelling in APT (Advanced Persistent Threat) attack scenarios. Additionally, this study innovatively incorporates time-series analysis to capture dynamic attack behaviors and enhances model robustness through multi-dimensional feature fusion. Overall, this research provides new technical approaches for cyber-attack attribution, significantly improving the capability to identify unknown attacks and laying a theoretical and technical foundation for constructing a more secure network environment.
Keywords: Network Attack Traceability；Behavior Feature Extraction；Machine Learning
引言 1
一、网络攻击溯源基础理论 1
(一)攻击溯源的概念与意义 1
(二)行为分析的基本原理 2
(三)特征提取的核心方法 2
二、行为分析技术在溯源中的应用 3
(一)数据采集与预处理技术 3
(二)异常行为检测模型构建 3
(三)行为模式的关联分析 4
三、特征提取的关键技术研究 4
(一)特征选择与降维方法 4
(二)动态特征提取算法设计 5
(三)特征表示与优化策略 5
四、溯源技术的实际应用与挑战 6
(一)实验环境与数据集构建 6
(二)溯源系统的性能评估 6
(三)当前技术面临的挑战 7
结论 7
参考文献 9
致谢 9
