基于数据流的实时网络攻击检测方法研究 -计算机科学与技术专业

摘要

随着互联网的飞速发展，网络安全问题亦随之增多，尤其是网络攻击手段的多样性和复杂性给网络安全带来了前所未有的挑战。传统的网络安全措施往往无法实时有效地防御这些攻击，因而基于数据流的实时网络攻击检测方法受到了研究者的广泛关注。这种方法能够对数据流进行快速处理与深度分析，实现对网络异常行为的即时识别和响应，有效提升网络安全防护能力。本文首先介绍了研究的背景和意义，随后分析了国内外在实时网络攻击检测方面的研究现状，明确了研究的目的。文章深入探讨了数据流模型与特性、实时攻击检测原理以及数据挖掘与机器学习算法等理论基础和关键技术。在特征提取与选择方面，文章详细阐述了数据流预处理、特征选择与优化以及实时特征更新机制。在实时网络攻击检测算法研究方面，文章重点研究了实时检测框架设计、实时分类与检测算法以及实时检测性能优化。最后，文章指出了基于数据流的实时网络攻击检测方法的未来研究方向，包括深度学习在实时检测中的进一步应用、跨域数据流关联分析与检测以及自适应与智能化的检测策略。

关键词：网络攻击检测；数据流模型；特征提取

Abstract

With the rapid development of the Internet, network security issues have also increased, especially the diversity and complexity of network attacks have brought unprecedented challenges to network security. Traditional network security measures often cannot effectively defend against these attacks in real time, so real-time network attack detection methods based on data streams have received widespread attention from researchers. This method can quickly process and deeply analyze data streams, achieve instant recognition and response to abnormal network behavior, and effectively enhance network security protection capabilities. This article first introduces the background and significance of the research, and then analyzes the current research status of real-time network attack detection at home and abroad, clarifying the purpose of the research. The article delves into the theoretical foundations and key technologies of data stream models and characteristics, real-time attack detection principles, as well as data mining and machine learning algorithms. In terms of feature extraction and selection, the article elaborates in detail on data stream preprocessing, feature selection and optimization, and real-time feature update mechanisms. In the research of real-time network attack detection algorithms, the article focuses on the design of real-time detection fr amework, real-time classification and detection algorithms, and optimization of real-time detection performance. Finally, the article points out the future research directions of real-time network attack detection methods based on data streams, including further application of deep learning in real-time detection, cross domain data stream correlation analysis and detection, and adaptive and intelligent detection strategies.

Keywords： network attack detection; Data flow model; feature extraction

摘要 I

Abstract II

一、引言 1

二、理论基础与关键技术 2

（一）数据流模型与特性 2

（二）实时攻击检测原理 2

（三）数据挖掘与机器学习算法 3

三、基于数据流的特征提取与选择 4

（一）数据流预处理 4

（二）特征选择与优化 4

（三）实时特征更新机制 5