网络流量分析中的异常检测技术研究
摘 要
随着互联网的迅猛发展，网络流量呈爆炸式增长，其中隐藏着大量异常行为，如网络攻击、非法入侵等，对网络安全构成严重威胁。本研究旨在深入探讨网络流量分析中的异常检测技术，以期构建高效准确的异常检测系统。基于此目的，采用多种先进的机器学习算法与传统统计方法相结合的方式进行研究。首先，收集并预处理大规模的真实网络流量数据集，通过特征工程提取有效特征；然后，利用支持向量机、随机森林等算法建立异常检测模型，并引入深度学习框架下的自编码器进一步提升检测性能。实验结果表明，所提方法在检测率方面较传统方法有显著提高，能够有效识别出各种类型的异常流量，包括已知和未知攻击模式。
关键词：网络流量异常检测 机器学习算法 特征工程
Abstract
With the rapid development of the Internet, the network traffic shows an explosive growth, which hides a large number of abnormal behaviors, such as network attacks, illegal intrusion, etc., which pose a serious threat to the network security. This study aims to explore the abnormality detection technology in network traffic analysis in order to build an efficient and accurate abnormality detection system. For this purpose, many advanced machine learning algorithms combine with traditional statistical methods. First, large-scale real network traffic datasets are collected and preprocessed, and effective features are extracted by feature engineering; then, abnormal detection model is built, and autoencoder under deep learning fr amework are introduced to further improve the detection performance. The experimental results show that the proposed method significantly improves the detection rate compared with the traditional method, and can effectively identify various types of abnormal traffic, including known and unknown attack patterns.
Keyword: Network traffic abnormality detection machine learning algorithm feature engineering
目 录
1绪论 1
1.1 研究背景及意义 1
1.2 国内外研究现状 1
1.3 本文研究方法 1
2网络流量特征分析 2
2.1 流量数据获取方式 2
2.2 正常流量特征提取 3
2.3 异常流量模式识别 3
3异常检测算法研究 4
3.1 基于统计的检测方法 4
3.2 基于机器学习的检测 5
3.3 混合检测算法设计 5
4实验验证与结果分析 6
4.1 实验环境搭建 6
4.2 检测性能评估 6
4.3 结果对比分析 7
5结论 8
参考文献 9
致谢 10
摘 要
随着互联网的迅猛发展，网络流量呈爆炸式增长，其中隐藏着大量异常行为，如网络攻击、非法入侵等，对网络安全构成严重威胁。本研究旨在深入探讨网络流量分析中的异常检测技术，以期构建高效准确的异常检测系统。基于此目的，采用多种先进的机器学习算法与传统统计方法相结合的方式进行研究。首先，收集并预处理大规模的真实网络流量数据集，通过特征工程提取有效特征；然后，利用支持向量机、随机森林等算法建立异常检测模型，并引入深度学习框架下的自编码器进一步提升检测性能。实验结果表明，所提方法在检测率方面较传统方法有显著提高，能够有效识别出各种类型的异常流量，包括已知和未知攻击模式。
关键词：网络流量异常检测 机器学习算法 特征工程
Abstract
With the rapid development of the Internet, the network traffic shows an explosive growth, which hides a large number of abnormal behaviors, such as network attacks, illegal intrusion, etc., which pose a serious threat to the network security. This study aims to explore the abnormality detection technology in network traffic analysis in order to build an efficient and accurate abnormality detection system. For this purpose, many advanced machine learning algorithms combine with traditional statistical methods. First, large-scale real network traffic datasets are collected and preprocessed, and effective features are extracted by feature engineering; then, abnormal detection model is built, and autoencoder under deep learning fr amework are introduced to further improve the detection performance. The experimental results show that the proposed method significantly improves the detection rate compared with the traditional method, and can effectively identify various types of abnormal traffic, including known and unknown attack patterns.
Keyword: Network traffic abnormality detection machine learning algorithm feature engineering
目 录
1绪论 1
1.1 研究背景及意义 1
1.2 国内外研究现状 1
1.3 本文研究方法 1
2网络流量特征分析 2
2.1 流量数据获取方式 2
2.2 正常流量特征提取 3
2.3 异常流量模式识别 3
3异常检测算法研究 4
3.1 基于统计的检测方法 4
3.2 基于机器学习的检测 5
3.3 混合检测算法设计 5
4实验验证与结果分析 6
4.1 实验环境搭建 6
4.2 检测性能评估 6
4.3 结果对比分析 7
5结论 8
参考文献 9
致谢 10
