摘 要
随着信息技术的迅猛发展，网络安全威胁日益复杂多样，传统单一模型在应对多变的网络攻击时存在分类准确率低、泛化能力差等问题。为此，本研究提出一种基于集成学习方法的网络安全攻击分类算法，旨在提高对不同类型网络攻击的识别精度和响应速度。该算法融合了多种机器学习模型的优势，通过构建弱分类器组合形成强分类器，利用Bagging与Boosting相结合的方式优化模型结构。实验采用KDD CUP 99数据集进行验证，在特征选择阶段引入信息增益比作为评价标准，确保输入特征的有效性。结果表明，所提算法相较于传统方法在分类准确率上提升了约15%，F1 - score达到0.92以上，并且对于未知攻击类型的检测能力显著增强。此外，该算法具有良好的可扩展性和鲁棒性，能够适应不同规模和类型的网络环境。
关键词：网络安全攻击分类 集成学习 Bagging与Boosting
Abstract
With the rapid development of information technology, network security threats are increasingly complex and diverse, and the traditional single model has problems such as low classification accuracy and poor generalization ability when dealing with changeable network attacks. To this end, this study proposes a cybersecurity attack classification algorithm based on integrated learning methods, aiming to improve the identification accuracy and response speed to different types of network attacks. This algorithm incorporates the advantages of multiple machine learning models, forms a strong classifier by constructing a weak classifier combination, and optimizes the model structure combined with Bagging and Boosting. The experiment was validated using the KDD CUP 99 dataset and introduced the information gain ratio as the evaluation criterion in the feature selection stage to ensure the validity of the input features. The results show that compared with the traditional method, the proposed algorithm improves the classification accuracy by about 15%, and achieves the F1-score by more than 0.92, and the detection ability for unknown attack types is significantly enhanced. Moreover, the algorithm is well scalable and robust and adapting to different sizes and types of network environments.
Keyword:Cybersecurity Attack Classification Ensemble Learning Bagging And Boosting
目 录
1绪论 1
1.1网络安全攻击分类的研究背景 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 2
2集成学习方法的理论基础 2
2.1集成学习的基本概念 2
2.2常见集成学习算法分析 3
2.3集成学习在网络安全中的应用 3
3网络安全攻击分类算法设计 4
3.1攻击特征提取与选择 4
3.2分类模型构建与优化 5
3.3算法性能评估指标 5
4实验验证与结果分析 6
4.1实验环境与数据集 6
4.2实验方案设计 7
4.3结果分析与讨论 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样，传统单一模型在应对多变的网络攻击时存在分类准确率低、泛化能力差等问题。为此，本研究提出一种基于集成学习方法的网络安全攻击分类算法，旨在提高对不同类型网络攻击的识别精度和响应速度。该算法融合了多种机器学习模型的优势，通过构建弱分类器组合形成强分类器，利用Bagging与Boosting相结合的方式优化模型结构。实验采用KDD CUP 99数据集进行验证，在特征选择阶段引入信息增益比作为评价标准，确保输入特征的有效性。结果表明，所提算法相较于传统方法在分类准确率上提升了约15%，F1 - score达到0.92以上，并且对于未知攻击类型的检测能力显著增强。此外，该算法具有良好的可扩展性和鲁棒性，能够适应不同规模和类型的网络环境。
关键词：网络安全攻击分类 集成学习 Bagging与Boosting
Abstract
With the rapid development of information technology, network security threats are increasingly complex and diverse, and the traditional single model has problems such as low classification accuracy and poor generalization ability when dealing with changeable network attacks. To this end, this study proposes a cybersecurity attack classification algorithm based on integrated learning methods, aiming to improve the identification accuracy and response speed to different types of network attacks. This algorithm incorporates the advantages of multiple machine learning models, forms a strong classifier by constructing a weak classifier combination, and optimizes the model structure combined with Bagging and Boosting. The experiment was validated using the KDD CUP 99 dataset and introduced the information gain ratio as the evaluation criterion in the feature selection stage to ensure the validity of the input features. The results show that compared with the traditional method, the proposed algorithm improves the classification accuracy by about 15%, and achieves the F1-score by more than 0.92, and the detection ability for unknown attack types is significantly enhanced. Moreover, the algorithm is well scalable and robust and adapting to different sizes and types of network environments.
Keyword:Cybersecurity Attack Classification Ensemble Learning Bagging And Boosting
目 录
1绪论 1
1.1网络安全攻击分类的研究背景 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 2
2集成学习方法的理论基础 2
2.1集成学习的基本概念 2
2.2常见集成学习算法分析 3
2.3集成学习在网络安全中的应用 3
3网络安全攻击分类算法设计 4
3.1攻击特征提取与选择 4
3.2分类模型构建与优化 5
3.3算法性能评估指标 5
4实验验证与结果分析 6
4.1实验环境与数据集 6
4.2实验方案设计 7
4.3结果分析与讨论 7
结论 8
参考文献 9
致谢 10
