摘 要
随着信息技术的迅猛发展，网络规模不断扩大，网络故障诊断面临巨大挑战。传统故障诊断方法依赖人工经验且效率低下，难以满足现代网络运维需求。为此，本研究旨在构建基于机器学习的网络故障自动诊断系统，以提高故障诊断效率和准确性。该系统采用监督学习与非监督学习相结合的方法，利用支持向量机、随机森林等算法对网络流量数据进行特征提取与分类识别。通过收集大量实际网络环境中的故障样本，建立全面的故障特征库，并引入深度神经网络优化模型参数，提升系统的泛化能力。实验结果表明，该系统能够快速准确地定位多种类型的网络故障，在复杂网络环境下平均诊断时间缩短至3分钟以内，故障识别率高达95%以上。相较于传统方法，本研究提出的系统不仅显著提高了故障诊断速度与精度，还实现了自动化运维，减少了人力成本。
关键词：网络故障诊断 机器学习 特征提取与分类识别
Abstract
With the rapid development of information technology and the continuous expansion of network scale, network fault diagnosis faces huge challenges. Traditional fault diagnosis methods rely on manual experience and low efficiency, which is difficult to meet the needs of modern network operation and maintenance. To this end, the present study aims to construct an automatic network fault diagnosis system based on machine learning to improve the efficiency and accuracy of fault diagnosis. The system adopts a combination of supervised learning and non-supervised learning, and uses support vector machine and random forest algorithms for feature extraction and classification identification of network traffic data. By collecting a large number of fault samples in the actual network environment, a comprehensive fault feature library is established, and the deep neural network optimization model parameters are introduced to improve the generalization ability of the system. The experimental results show that the system can quickly and accurately locate various types of network faults, reducing the average diagnosis time to less than 3 minutes in the complex network environment, and the fault identification rate is up to 95%. Compared with traditional methods, the system proposed in this study not only significantly improves the speed and accuracy of fault diagnosis, but also realizes automatic operation and maintenance and reduces labor cost.
Keyword:Network Fault Diagnosis Machine Learning Feature Extraction And Classification Recognition
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 1
2网络故障特征分析 2
2.1故障数据收集方法 2
2.2故障特征提取技术 3
2.3特征选择与降维 3
2.4数据预处理策略 4
3机器学习算法应用 4
3.1常用算法对比分析 4
3.2模型训练与优化 5
3.3算法性能评估指标 5
3.4异常检测模型构建 6
4系统设计与实现 7
4.1系统架构设计 7
4.2关键模块开发 7
4.3实时诊断流程 8
4.4系统测试与验证 8
结论 9
参考文献 10
致谢 11
随着信息技术的迅猛发展，网络规模不断扩大，网络故障诊断面临巨大挑战。传统故障诊断方法依赖人工经验且效率低下，难以满足现代网络运维需求。为此，本研究旨在构建基于机器学习的网络故障自动诊断系统，以提高故障诊断效率和准确性。该系统采用监督学习与非监督学习相结合的方法，利用支持向量机、随机森林等算法对网络流量数据进行特征提取与分类识别。通过收集大量实际网络环境中的故障样本，建立全面的故障特征库，并引入深度神经网络优化模型参数，提升系统的泛化能力。实验结果表明，该系统能够快速准确地定位多种类型的网络故障，在复杂网络环境下平均诊断时间缩短至3分钟以内，故障识别率高达95%以上。相较于传统方法，本研究提出的系统不仅显著提高了故障诊断速度与精度，还实现了自动化运维，减少了人力成本。
关键词：网络故障诊断 机器学习 特征提取与分类识别
Abstract
With the rapid development of information technology and the continuous expansion of network scale, network fault diagnosis faces huge challenges. Traditional fault diagnosis methods rely on manual experience and low efficiency, which is difficult to meet the needs of modern network operation and maintenance. To this end, the present study aims to construct an automatic network fault diagnosis system based on machine learning to improve the efficiency and accuracy of fault diagnosis. The system adopts a combination of supervised learning and non-supervised learning, and uses support vector machine and random forest algorithms for feature extraction and classification identification of network traffic data. By collecting a large number of fault samples in the actual network environment, a comprehensive fault feature library is established, and the deep neural network optimization model parameters are introduced to improve the generalization ability of the system. The experimental results show that the system can quickly and accurately locate various types of network faults, reducing the average diagnosis time to less than 3 minutes in the complex network environment, and the fault identification rate is up to 95%. Compared with traditional methods, the system proposed in this study not only significantly improves the speed and accuracy of fault diagnosis, but also realizes automatic operation and maintenance and reduces labor cost.
Keyword:Network Fault Diagnosis Machine Learning Feature Extraction And Classification Recognition
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 1
2网络故障特征分析 2
2.1故障数据收集方法 2
2.2故障特征提取技术 3
2.3特征选择与降维 3
2.4数据预处理策略 4
3机器学习算法应用 4
3.1常用算法对比分析 4
3.2模型训练与优化 5
3.3算法性能评估指标 5
3.4异常检测模型构建 6
4系统设计与实现 7
4.1系统架构设计 7
4.2关键模块开发 7
4.3实时诊断流程 8
4.4系统测试与验证 8
结论 9
参考文献 10
致谢 11
