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。本研究旨在构建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。通过分析现有入侵检测技术的局限性，提出了一种融合多种机器学习算法的综合框架，该框架结合了监督学习与非监督学习的优势，采用特征选择优化算法对原始数据进行预处理，有效降低了维度灾难问题。实验中使用KDD CUP 99等标准数据集进行测试，结果表明所提出的模型在检测率、误报率等关键指标上均优于传统方法。特别是针对未知攻击类型的检测能力显著提升，实现了对新型威胁的快速响应。此外，本研究还引入了在线学习机制，使系统能够动态更新模型参数，适应不断变化的网络环境。这一创新不仅提高了系统的鲁棒性和泛化能力，也为实际应用提供了可靠的理论依据和技术支持，为构建智能化、自适应的网络安全防护体系奠定了坚实基础。
关键词：网络入侵检测系统；机器学习；特征选择优化
Abstract
With the rapid development of information technology, cyber security threats have become increasingly severe, posing significant challenges to traditional intrusion detection systems in应对 complex and evolving attack patterns. This study aims to construct a network intrusion detection system based on machine learning to enhance detection efficiency and accuracy. By analyzing the limitations of existing intrusion detection technologies, this research proposes an integrated fr amework that combines multiple machine learning algorithms, leveraging the advantages of both supervised and unsupervised learning. The fr amework employs feature selection optimization algorithms for preprocessing raw data, effectively mitigating the curse of dimensionality. Standard datasets such as KDD CUP 99 were used in experiments, and the results demonstrate that the proposed model outperforms traditional methods in key metrics including detection rate and false positive rate. Notably, the detection capability for unknown attack types has been significantly improved, enabling rapid response to emerging threats. Furthermore, this study introduces an online learning mechanism, allowing the system to dynamically update model parameters and adapt to the ever-changing network environment. This innovation not only enhances the robustness and generalization ability of the system but also provides reliable theoretical support and technical underpinnings for practical applications, laying a solid foundation for building intelligent and adaptive cyber security protection systems.
Keywords：Network Intrusion Detection System; Machine Learning; Feature Selection Optimization
目 录
摘要 I
Abstract II
一、绪论 1
(一) 网络入侵检测的研究背景与意义 1
(二) 国内外研究现状综述 1
(三) 本文研究方法概述 2
二、机器学习算法在入侵检测中的应用 2
(一) 常用机器学习算法介绍 2
(二) 算法选择与优化策略 3
(三) 入侵检测中的特征提取与选择 4
三、数据集构建与预处理 4
(一) 入侵检测数据集来源 4
(二) 数据清洗与标准化 5
(三) 数据标注与分类 6
四、系统设计与实现 7
(一) 系统架构设计原则 7
(二) 关键模块功能分析 7
(三) 实验环境搭建与配置 8
结 论 10
参考文献 11
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。本研究旨在构建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。通过分析现有入侵检测技术的局限性，提出了一种融合多种机器学习算法的综合框架，该框架结合了监督学习与非监督学习的优势，采用特征选择优化算法对原始数据进行预处理，有效降低了维度灾难问题。实验中使用KDD CUP 99等标准数据集进行测试，结果表明所提出的模型在检测率、误报率等关键指标上均优于传统方法。特别是针对未知攻击类型的检测能力显著提升，实现了对新型威胁的快速响应。此外，本研究还引入了在线学习机制，使系统能够动态更新模型参数，适应不断变化的网络环境。这一创新不仅提高了系统的鲁棒性和泛化能力，也为实际应用提供了可靠的理论依据和技术支持，为构建智能化、自适应的网络安全防护体系奠定了坚实基础。
关键词：网络入侵检测系统；机器学习；特征选择优化
Abstract
With the rapid development of information technology, cyber security threats have become increasingly severe, posing significant challenges to traditional intrusion detection systems in应对 complex and evolving attack patterns. This study aims to construct a network intrusion detection system based on machine learning to enhance detection efficiency and accuracy. By analyzing the limitations of existing intrusion detection technologies, this research proposes an integrated fr amework that combines multiple machine learning algorithms, leveraging the advantages of both supervised and unsupervised learning. The fr amework employs feature selection optimization algorithms for preprocessing raw data, effectively mitigating the curse of dimensionality. Standard datasets such as KDD CUP 99 were used in experiments, and the results demonstrate that the proposed model outperforms traditional methods in key metrics including detection rate and false positive rate. Notably, the detection capability for unknown attack types has been significantly improved, enabling rapid response to emerging threats. Furthermore, this study introduces an online learning mechanism, allowing the system to dynamically update model parameters and adapt to the ever-changing network environment. This innovation not only enhances the robustness and generalization ability of the system but also provides reliable theoretical support and technical underpinnings for practical applications, laying a solid foundation for building intelligent and adaptive cyber security protection systems.
Keywords：Network Intrusion Detection System; Machine Learning; Feature Selection Optimization
目 录
摘要 I
Abstract II
一、绪论 1
(一) 网络入侵检测的研究背景与意义 1
(二) 国内外研究现状综述 1
(三) 本文研究方法概述 2
二、机器学习算法在入侵检测中的应用 2
(一) 常用机器学习算法介绍 2
(二) 算法选择与优化策略 3
(三) 入侵检测中的特征提取与选择 4
三、数据集构建与预处理 4
(一) 入侵检测数据集来源 4
(二) 数据清洗与标准化 5
(三) 数据标注与分类 6
四、系统设计与实现 7
(一) 系统架构设计原则 7
(二) 关键模块功能分析 7
(三) 实验环境搭建与配置 8
结 论 10
参考文献 11
