摘 要
随着信息技术的迅猛发展，网络安全威胁日益严峻，传统入侵检测系统在应对复杂多变的攻击模式时面临诸多挑战。为此，本研究旨在设计并实现基于机器学习的网络入侵检测系统，以提高检测效率和准确性。该系统采用多种机器学习算法，包括支持向量机、随机森林和深度神经网络等，对网络流量数据进行特征提取与分析。通过构建混合特征选择机制，融合统计特征、内容特征及时序特征，有效提升了模型的泛化能力。实验结果表明，在KDD Cup 99和NSL - KDD等标准数据集上，所提系统的检测率分别达到98.7%和96.3%，误报率控制在2.1%以内。相较于传统方法，该系统不仅能够准确识别已知攻击类型，还能有效发现新型未知攻击，展现出良好的适应性和鲁棒性。此外，系统引入在线学习机制，可实时更新模型参数，确保其始终处于最优状态。本研究的主要贡献在于提出了一种高效稳定的网络入侵检测方案，为保障现代网络环境安全提供了新的思路和技术手段。
关键词：网络入侵检测；机器学习算法；特征选择机制
Abstract
With the rapid development of information technology, the network security threats are increasingly severe, and the traditional intrusion detection system faces many challenges in dealing with the complex and changeable attack modes. To this end, this study aims to design and implement a machine learning-based network intrusion detection system to improve detection efficiency and accuracy. The system uses a variety of machine learning algorithms, including support vector machine, random forest and deep neural network, to extract features and analyze the network traffic data. By constructing the mixed feature selection mechanism, integrating the statistical features and content features, the generalization ability of the model is effectively improved. The experimental results show that on the standard data sets such as KDD Cup 99 and NSL-KDD, the detection rate of the proposed system reaches 98.7% and 96.3%, respectively, and the false alarm rate is controlled within 2.1%. Compared with the traditional method, the system can not only accurately identify the known attacks type, but also effectively find the new unknown attacks, showing good adaptability and robustness. In addition, the system introduces an online learning mechanism to update the model parameters in real time to ensure that it is always in the optimal state. The main contribution of this study is to propose an efficient and stable network intrusion detection scheme, which provides new ideas and technical means to guarantee the security of modern network environment.
Keywords：Network Intrusion Detection；Machine Learning Algorithm；Feature Selection Mechanism
目 录
引 言 1
第一章 网络入侵检测系统概述 2
1.1 网络安全现状分析 2
1.2 入侵检测技术发展 2
1.3 机器学习在 3
第二章 系统需求与架构设计 5
2.1 系统架构规划 5
2.2 关键模块设计 5
第三章 机器学习算法选择与优化 7
3.1 常用算法对比分析 7
3.2 特征选择与提取 7
3.3 模型训练与调优 8
第四章 系统实现与性能评估 10
4.1 系统开发环境搭建 10
4.2 实验数据集构建 10
4.3 性能测试与结果分析 11
结 论 13
参考文献 14
致 谢 15
随着信息技术的迅猛发展，网络安全威胁日益严峻，传统入侵检测系统在应对复杂多变的攻击模式时面临诸多挑战。为此，本研究旨在设计并实现基于机器学习的网络入侵检测系统，以提高检测效率和准确性。该系统采用多种机器学习算法，包括支持向量机、随机森林和深度神经网络等，对网络流量数据进行特征提取与分析。通过构建混合特征选择机制，融合统计特征、内容特征及时序特征，有效提升了模型的泛化能力。实验结果表明，在KDD Cup 99和NSL - KDD等标准数据集上，所提系统的检测率分别达到98.7%和96.3%，误报率控制在2.1%以内。相较于传统方法，该系统不仅能够准确识别已知攻击类型，还能有效发现新型未知攻击，展现出良好的适应性和鲁棒性。此外，系统引入在线学习机制，可实时更新模型参数，确保其始终处于最优状态。本研究的主要贡献在于提出了一种高效稳定的网络入侵检测方案，为保障现代网络环境安全提供了新的思路和技术手段。
关键词：网络入侵检测；机器学习算法；特征选择机制
Abstract
With the rapid development of information technology, the network security threats are increasingly severe, and the traditional intrusion detection system faces many challenges in dealing with the complex and changeable attack modes. To this end, this study aims to design and implement a machine learning-based network intrusion detection system to improve detection efficiency and accuracy. The system uses a variety of machine learning algorithms, including support vector machine, random forest and deep neural network, to extract features and analyze the network traffic data. By constructing the mixed feature selection mechanism, integrating the statistical features and content features, the generalization ability of the model is effectively improved. The experimental results show that on the standard data sets such as KDD Cup 99 and NSL-KDD, the detection rate of the proposed system reaches 98.7% and 96.3%, respectively, and the false alarm rate is controlled within 2.1%. Compared with the traditional method, the system can not only accurately identify the known attacks type, but also effectively find the new unknown attacks, showing good adaptability and robustness. In addition, the system introduces an online learning mechanism to update the model parameters in real time to ensure that it is always in the optimal state. The main contribution of this study is to propose an efficient and stable network intrusion detection scheme, which provides new ideas and technical means to guarantee the security of modern network environment.
Keywords：Network Intrusion Detection；Machine Learning Algorithm；Feature Selection Mechanism
目 录
引 言 1
第一章 网络入侵检测系统概述 2
1.1 网络安全现状分析 2
1.2 入侵检测技术发展 2
1.3 机器学习在 3
第二章 系统需求与架构设计 5
2.1 系统架构规划 5
2.2 关键模块设计 5
第三章 机器学习算法选择与优化 7
3.1 常用算法对比分析 7
3.2 特征选择与提取 7
3.3 模型训练与调优 8
第四章 系统实现与性能评估 10
4.1 系统开发环境搭建 10
4.2 实验数据集构建 10
4.3 性能测试与结果分析 11
结 论 13
参考文献 14
致 谢 15
