摘 要
随着信息技术的迅猛发展，网络攻击事件频发且手段日益复杂化、隐蔽化，对网络安全构成了严重威胁。为此，本研究旨在基于大数据分析实现对网络攻击行为模式的有效识别。以海量网络数据为研究对象，利用大数据技术中的数据挖掘、机器学习等方法构建网络攻击行为模式识别模型。通过收集并预处理来自不同网络环境下的流量数据，提取特征向量，采用多种算法进行训练与测试，包括但不限于支持向量机、随机森林等。结果表明该模型能够准确区分正常网络行为与攻击行为，对于常见的如DDoS攻击、SQL注入攻击等识别率均达到较高水平。创新之处在于融合多源异构数据，从更全面的角度刻画网络攻击行为特征，并且针对数据的动态变化特性提出自适应更新机制以保证模型的时效性。这不仅有助于深入理解网络攻击的本质规律，还为网络安全防御体系提供了新的思路和技术支撑，可及时发现潜在威胁并采取应对措施，从而提升网络安全防护能力。
关键词：网络攻击行为模式识别；大数据分析；特征向量提取
Abstract
With the rapid development of information technology, network attacks occur frequently and the means are increasingly complicated and hidden, which poses a serious threat to network security. To this end, this study aims to realize effective identification of cyberattack behavior patterns based on big data analysis. With massive network data as the research ob ject, the data mining and machine learning are used in big data technology. By collecting and preprocessing traffic data from different network environments, feature vectors are extracted, and various algorithms are used for training and testing, including but not limited to support vector machine, random forest, etc. The results show that the model can accurately distinguish normal network behavior from attack behavior, and the recognition rate of common DDoS attack and SQL injection attack reaches a high level. The innovation lies in the fusion of multi-source heterogeneous data, describing the characteristics of network attack behavior from a more comprehensive perspective, and putting forward an adaptive update mechanism according to the dynamic change characteristics of the data to ensure the timeliness of the model. This not only helps to have a deep understanding of the essential laws of network attacks, but also provides new ideas and technical support for the network security defense system, which can find potential threats in time and take measures to deal with them, so as to improve the network security protection capability.
Keywords：Network Attack Behavior Pattern Recognition；Big Data Analysis；Feature Vector Extraction
目 录
引 言 1
第一章 网络攻击行为特征分析 2
1.1 攻击行为数据来源 2
1.2 特征提取方法研究 2
1.3 行为模式分类体系 3
第二章 大数据分析技术应用 5
2.1 数据预处理技术 5
2.2 关联规则挖掘算法 5
2.3 实时数据分析方法 6
第三章 攻击模式识别模型构建 8
3.1 机器学习算法选择 8
3.2 模型训练与优化 8
3.3 模型评估指标体系 9
第四章 系统实现与案例分析 11
4.1 系统架构设计 11
4.2 关键技术实现 11
4.3 实际应用案例研究 12
结 论 14
参考文献 15
致 谢 16
随着信息技术的迅猛发展，网络攻击事件频发且手段日益复杂化、隐蔽化，对网络安全构成了严重威胁。为此，本研究旨在基于大数据分析实现对网络攻击行为模式的有效识别。以海量网络数据为研究对象，利用大数据技术中的数据挖掘、机器学习等方法构建网络攻击行为模式识别模型。通过收集并预处理来自不同网络环境下的流量数据，提取特征向量，采用多种算法进行训练与测试，包括但不限于支持向量机、随机森林等。结果表明该模型能够准确区分正常网络行为与攻击行为，对于常见的如DDoS攻击、SQL注入攻击等识别率均达到较高水平。创新之处在于融合多源异构数据，从更全面的角度刻画网络攻击行为特征，并且针对数据的动态变化特性提出自适应更新机制以保证模型的时效性。这不仅有助于深入理解网络攻击的本质规律，还为网络安全防御体系提供了新的思路和技术支撑，可及时发现潜在威胁并采取应对措施，从而提升网络安全防护能力。
关键词：网络攻击行为模式识别；大数据分析；特征向量提取
Abstract
With the rapid development of information technology, network attacks occur frequently and the means are increasingly complicated and hidden, which poses a serious threat to network security. To this end, this study aims to realize effective identification of cyberattack behavior patterns based on big data analysis. With massive network data as the research ob ject, the data mining and machine learning are used in big data technology. By collecting and preprocessing traffic data from different network environments, feature vectors are extracted, and various algorithms are used for training and testing, including but not limited to support vector machine, random forest, etc. The results show that the model can accurately distinguish normal network behavior from attack behavior, and the recognition rate of common DDoS attack and SQL injection attack reaches a high level. The innovation lies in the fusion of multi-source heterogeneous data, describing the characteristics of network attack behavior from a more comprehensive perspective, and putting forward an adaptive update mechanism according to the dynamic change characteristics of the data to ensure the timeliness of the model. This not only helps to have a deep understanding of the essential laws of network attacks, but also provides new ideas and technical support for the network security defense system, which can find potential threats in time and take measures to deal with them, so as to improve the network security protection capability.
Keywords：Network Attack Behavior Pattern Recognition；Big Data Analysis；Feature Vector Extraction
目 录
引 言 1
第一章 网络攻击行为特征分析 2
1.1 攻击行为数据来源 2
1.2 特征提取方法研究 2
1.3 行为模式分类体系 3
第二章 大数据分析技术应用 5
2.1 数据预处理技术 5
2.2 关联规则挖掘算法 5
2.3 实时数据分析方法 6
第三章 攻击模式识别模型构建 8
3.1 机器学习算法选择 8
3.2 模型训练与优化 8
3.3 模型评估指标体系 9
第四章 系统实现与案例分析 11
4.1 系统架构设计 11
4.2 关键技术实现 11
4.3 实际应用案例研究 12
结 论 14
参考文献 15
致 谢 16
