基于大数据分析的网络攻击检测技术研究 -计算机科学与技术专业

摘要

随着大数据时代的到来，网络空间中的数据量呈爆炸式增长，为网络攻击检测提供了新的机遇与挑战。本文首先概述了研究背景与意义，指出大数据分析在网络安全中的重要性，并综述了国内外相关研究现状，明确了本文的研究目的。在大数据分析基础部分，文章详细阐述了大数据的定义、特征以及关键处理技术，包括数据采集、存储、处理和分析等环节。随后，文章深入分析了网络攻击检测技术的演进，从传统攻击检测技术出发，逐步过渡到基于行为的检测技术和基于大数据的攻击检测技术。基于大数据的攻击检测技术以其强大的数据处理能力和模式识别能力，成为当前研究的热点。在此基础上，本文设计了一个基于大数据的网络攻击检测框架，该框架涵盖了数据采集与预处理、攻击检测模型构建、实时检测与响应机制等多个关键环节。在数据采集与预处理阶段，通过多元化数据源集成、数据清洗与规范化以及特征提取与选择，确保输入数据的质量和有效性。在攻击检测模型构建阶段，采用机器学习算法和异常检测与模式识别技术，构建高效准确的检测模型，并通过模型训练与优化策略不断提升其性能。在实时检测与响应机制方面，通过实时数据流处理、攻击预警与响应流程以及检测结果可视化与报告，实现对网络攻击的及时发现和有效应对。

关键词：大数据分析；网络攻击检测；机器学习；数据预处理

Abstract

With the advent of the big data era, the amount of data in cyberspace is exploding, providing new opportunities and challenges for network attack detection. This article first outlines the research background and significance, points out the importance of big data analysis in network security, and summarizes the relevant research status at home and abroad, clarifying the research purpose of this article. In the basic section of big data analysis, the article elaborates on the definition, characteristics, and key processing technologies of big data, including data collection, storage, processing, and analysis. Subsequently, the article delved into the evolution of network attack detection technology, starting from traditional attack detection techniques and gradually transitioning to behavior based detection techniques and big data based attack detection techniques. The attack detection technology based on big data has become a hot research topic due to its powerful data processing and pattern recognition capabilities. On this basis, this article designs a network attack detection fr amework based on big data, which covers multiple key aspects such as data collection and preprocessing, attack detection model construction, real-time detection and response mechanism. In the data collection and preprocessing stage, the quality and effectiveness of input data are ensured through diversified data source integration, data cleaning and standardization, and feature extraction and selection. In the stage of building attack detection models, machine learning algorithms and anomaly detection and pattern recognition techniques are used to construct efficient and accurate detection models, and their performance is continuously improved through model training and optimization strategies. In terms of real-time detection and response mechanisms, timely detection and effective response to network attacks are achieved through real-time data stream processing, attack warning and response processes, and visualization and reporting of detection results.

Keywords： Big data analysis; Network attack detection; Machine learning; Data preprocessing

摘要 I

Abstract II

一、引言 1

二、大数据分析基础 2

（一）大数据定义与特征 2

（二）大数据处理技术概述 2

（三）大数据分析在网络安全中的应用 2

三、网络攻击检测技术 4

（一）传统攻击检测技术简介 4

（二）基于行为的检测技术 4

（三）基于大数据的攻击检测技术原理 4