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，传统入侵检测系统在应对新型攻击时面临诸多挑战。为此，本文提出一种基于深度学习的网络入侵检测系统设计，旨在提高入侵检测的准确性和实时性。该研究利用深度神经网络强大的特征提取能力，构建了包括数据预处理、特征选择、模型训练与优化以及结果评估在内的完整框架。具体而言，在数据预处理阶段，采用标准化和归一化方法确保输入数据质量；特征选择方面，通过主成分分析等技术筛选出最具代表性的特征向量；模型选用卷积神经网络结合长短时记忆网络结构，以适应不同类型流量模式的学习需求。
关键词：网络入侵检测系统 深度学习 特征选择
Abstract
With the rapid development of information technology, 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 paper proposes a deep learning based network intrusion detection system design aiming to improve the accuracy and real-time performance of intrusion detection. This study uses the powerful feature extraction capability of deep neural network to construct a complete fr amework including data preprocessing, feature selection, model training and optimization, and outcome evaluation. Specifically, in the data pre-processing stage, standardization and normalization methods are adopted to ensure the input data quality; in the feature selection, the most representative feature vector is selected through principal component analysis and other technologies; the model selects convolutional neural network and long-short memory network structure to meet the learning requirements of different types of traffic modes.
Keyword:Network Intrusion Detection System Deep Learning Feature Selection
目 录
1绪论 1
1.1网络入侵检测系统研究背景与意义 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深度学习技术在入侵检测中的应用 2
2.1深度学习算法原理分析 2
2.2入侵检测常用深度学习模型 3
2.3数据预处理与特征提取方法 3
3基于深度学习的入侵检测系统架构设计 4
3.1系统整体架构规划 4
3.2关键模块功能设计 5
3.3系统性能优化策略 5
4实验验证与结果分析 6
4.1实验环境搭建与数据集选择 6
4.2性能评估指标体系构建 7
4.3实验结果对比分析 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变，传统入侵检测系统在应对新型攻击时面临诸多挑战。为此，本文提出一种基于深度学习的网络入侵检测系统设计，旨在提高入侵检测的准确性和实时性。该研究利用深度神经网络强大的特征提取能力，构建了包括数据预处理、特征选择、模型训练与优化以及结果评估在内的完整框架。具体而言，在数据预处理阶段，采用标准化和归一化方法确保输入数据质量；特征选择方面，通过主成分分析等技术筛选出最具代表性的特征向量；模型选用卷积神经网络结合长短时记忆网络结构，以适应不同类型流量模式的学习需求。
关键词：网络入侵检测系统 深度学习 特征选择
Abstract
With the rapid development of information technology, 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 paper proposes a deep learning based network intrusion detection system design aiming to improve the accuracy and real-time performance of intrusion detection. This study uses the powerful feature extraction capability of deep neural network to construct a complete fr amework including data preprocessing, feature selection, model training and optimization, and outcome evaluation. Specifically, in the data pre-processing stage, standardization and normalization methods are adopted to ensure the input data quality; in the feature selection, the most representative feature vector is selected through principal component analysis and other technologies; the model selects convolutional neural network and long-short memory network structure to meet the learning requirements of different types of traffic modes.
Keyword:Network Intrusion Detection System Deep Learning Feature Selection
目 录
1绪论 1
1.1网络入侵检测系统研究背景与意义 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深度学习技术在入侵检测中的应用 2
2.1深度学习算法原理分析 2
2.2入侵检测常用深度学习模型 3
2.3数据预处理与特征提取方法 3
3基于深度学习的入侵检测系统架构设计 4
3.1系统整体架构规划 4
3.2关键模块功能设计 5
3.3系统性能优化策略 5
4实验验证与结果分析 6
4.1实验环境搭建与数据集选择 6
4.2性能评估指标体系构建 7
4.3实验结果对比分析 7
结论 8
参考文献 9
致谢 10
