摘 要
随着信息技术的迅猛发展，局域网在企业、教育机构等各个领域得到广泛应用，但同时也面临着日益复杂的网络安全威胁，异常流量成为影响网络稳定与安全的重要因素。本研究旨在针对局域网环境下的异常流量检测技术展开深入探讨，以提高网络的安全性和可靠性。基于对现有异常流量检测技术的分析，提出一种融合机器学习算法与传统统计方法的混合检测模型，该模型能够有效识别已知和未知类型的异常流量。通过构建模拟局域网环境并引入多种类型的真实流量数据进行实验验证，结果表明所提出的混合检测模型具有较高的准确率和较低的误报率，在处理大规模数据时仍能保持良好的性能。
关键词：局域网安全 异常流量检测 机器学习
Abstract
With the rapid development of information technology, LAN has been widely used in enterprises, educational institutions and other fields, but at the same time, it is also facing increasingly complex network security threats, and abnormal traffic has become an important factor affecting network stability and security. This study aims to explore the abnormal traffic detection technology in the LAN environment to improve the network security and reliability. Based on the analysis of existing anomalous flow detection techniques, a hybrid detection model integrating machine learning algorithm and traditional statistical method is proposed, which can effectively identify known and unknown types of anomalous flow. By building a simulated LAN environment and introducing multiple types of real traffic data, the results show that the proposed hybrid detection model has high accuracy and low false positive rate, and still maintains good performance when processing large-scale data.
Keyword:Local Area Network Security Anomaly Traffic Detection Machine Learning
目 录
1绪论 1
1.1局域网异常流量检测的背景与意义 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局域网流量特征分析 2
2.1正常流量模式识别 2
2.2异常流量类型划分 3
2.3流量特征提取方法 3
3异常流量检测技术 4
3.1基于统计的检测方法 4
3.2基于机器学习的检测模型 5
3.3混合检测技术应用 5
4检测系统设计与实现 6
4.1系统架构设计原则 6
4.2关键模块功能实现 6
4.3实验环境与测试结果 7
结论 7
参考文献 9
致谢 10
随着信息技术的迅猛发展，局域网在企业、教育机构等各个领域得到广泛应用，但同时也面临着日益复杂的网络安全威胁，异常流量成为影响网络稳定与安全的重要因素。本研究旨在针对局域网环境下的异常流量检测技术展开深入探讨，以提高网络的安全性和可靠性。基于对现有异常流量检测技术的分析，提出一种融合机器学习算法与传统统计方法的混合检测模型，该模型能够有效识别已知和未知类型的异常流量。通过构建模拟局域网环境并引入多种类型的真实流量数据进行实验验证，结果表明所提出的混合检测模型具有较高的准确率和较低的误报率，在处理大规模数据时仍能保持良好的性能。
关键词：局域网安全 异常流量检测 机器学习
Abstract
With the rapid development of information technology, LAN has been widely used in enterprises, educational institutions and other fields, but at the same time, it is also facing increasingly complex network security threats, and abnormal traffic has become an important factor affecting network stability and security. This study aims to explore the abnormal traffic detection technology in the LAN environment to improve the network security and reliability. Based on the analysis of existing anomalous flow detection techniques, a hybrid detection model integrating machine learning algorithm and traditional statistical method is proposed, which can effectively identify known and unknown types of anomalous flow. By building a simulated LAN environment and introducing multiple types of real traffic data, the results show that the proposed hybrid detection model has high accuracy and low false positive rate, and still maintains good performance when processing large-scale data.
Keyword:Local Area Network Security Anomaly Traffic Detection Machine Learning
目 录
1绪论 1
1.1局域网异常流量检测的背景与意义 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局域网流量特征分析 2
2.1正常流量模式识别 2
2.2异常流量类型划分 3
2.3流量特征提取方法 3
3异常流量检测技术 4
3.1基于统计的检测方法 4
3.2基于机器学习的检测模型 5
3.3混合检测技术应用 5
4检测系统设计与实现 6
4.1系统架构设计原则 6
4.2关键模块功能实现 6
4.3实验环境与测试结果 7
结论 7
参考文献 9
致谢 10
