摘要
随着信息技术的迅猛发展，网络安全威胁日益复杂多变，传统的入侵检测系统在应对新型攻击时面临诸多挑战。为此，本研究旨在构建一种基于机器学习的网络入侵检测模型，以提高入侵检测的准确性和实时性。该研究选取了多种经典的机器学习算法，包括支持向量机、随机森林和深度神经网络等，并结合特征选择技术对KDD CUP 99数据集进行预处理，从中提取出最具代表性的特征用于训练和测试模型。实验结果表明，所提出的模型在检测率方面显著优于传统方法，能够有效识别已知和未知类型的网络攻击，特别是对于低频次、隐蔽性强的攻击行为具有较高的敏感度。此外，通过引入在线学习机制，使模型具备了自适应更新能力，可及时响应不断变化的网络环境。这一创新性设计不仅提升了系统的整体性能，还为后续研究提供了新的思路与方向，对推动智能网络安全防护体系的发展具有重要意义。
关键词：网络入侵检测；机器学习；特征选择
Abstract
With the rapid development of information technology, the network security threats are increasingly complex and changeable, and the traditional intrusion detection system faces many challenges in dealing with new attacks. To this end, this study aims to construct a machine learning-based model for network intrusion detection to improve the accuracy and real-time performance of intrusion detection. In this study, a variety of classical machine learning algorithms, including support vector machine, random forest and deep neural network, were combined with feature selection techniques, from which the most representative features were extracted for training and testing models. The experimental results show that the proposed model significantly outperforms the traditional methods in detection rate and can effectively identify known and unknown types of network attacks, especially for low-frequency and covert attacks. In addition, through the introduction of online learning mechanism, the model has the adaptive update ability, can timely respond to the changing network environment. This innovative design not only improves the overall performance of the system, but also provides new ideas and direction for the follow-up research, which is of great significance to promote the development of intelligent network security protection system.
Keywords：Network Intrusion Detection; Machine Learning; Feature Selection
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、基于机器学习的模型选择 2
(一) 入侵检测常用算法分析 2
(二) 机器学习算法对比研究 3
(三) 模型选择标准与依据 4
三、数据预处理与特征提取 4
(一) 网络流量数据获取 4
(二) 数据清洗与预处理 5
(三) 特征选择与降维技术 6
四、入侵检测模型构建与优化 6
(一) 模型架构设计原则 7
(二) 参数调优与性能评估 7
(三) 模型泛化能力提升 8
结 论 10
参考文献 11
随着信息技术的迅猛发展，网络安全威胁日益复杂多变，传统的入侵检测系统在应对新型攻击时面临诸多挑战。为此，本研究旨在构建一种基于机器学习的网络入侵检测模型，以提高入侵检测的准确性和实时性。该研究选取了多种经典的机器学习算法，包括支持向量机、随机森林和深度神经网络等，并结合特征选择技术对KDD CUP 99数据集进行预处理，从中提取出最具代表性的特征用于训练和测试模型。实验结果表明，所提出的模型在检测率方面显著优于传统方法，能够有效识别已知和未知类型的网络攻击，特别是对于低频次、隐蔽性强的攻击行为具有较高的敏感度。此外，通过引入在线学习机制，使模型具备了自适应更新能力，可及时响应不断变化的网络环境。这一创新性设计不仅提升了系统的整体性能，还为后续研究提供了新的思路与方向，对推动智能网络安全防护体系的发展具有重要意义。
关键词：网络入侵检测；机器学习；特征选择
Abstract
With the rapid development of information technology, the network security threats are increasingly complex and changeable, and the traditional intrusion detection system faces many challenges in dealing with new attacks. To this end, this study aims to construct a machine learning-based model for network intrusion detection to improve the accuracy and real-time performance of intrusion detection. In this study, a variety of classical machine learning algorithms, including support vector machine, random forest and deep neural network, were combined with feature selection techniques, from which the most representative features were extracted for training and testing models. The experimental results show that the proposed model significantly outperforms the traditional methods in detection rate and can effectively identify known and unknown types of network attacks, especially for low-frequency and covert attacks. In addition, through the introduction of online learning mechanism, the model has the adaptive update ability, can timely respond to the changing network environment. This innovative design not only improves the overall performance of the system, but also provides new ideas and direction for the follow-up research, which is of great significance to promote the development of intelligent network security protection system.
Keywords：Network Intrusion Detection; Machine Learning; Feature Selection
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、基于机器学习的模型选择 2
(一) 入侵检测常用算法分析 2
(二) 机器学习算法对比研究 3
(三) 模型选择标准与依据 4
三、数据预处理与特征提取 4
(一) 网络流量数据获取 4
(二) 数据清洗与预处理 5
(三) 特征选择与降维技术 6
四、入侵检测模型构建与优化 6
(一) 模型架构设计原则 7
(二) 参数调优与性能评估 7
(三) 模型泛化能力提升 8
结 论 10
参考文献 11
