基于大数据分析的网络安全威胁检测系统研究

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doi:10.3969/J.ISSN.1672-7274.2025.06.017
中图分类号:TP3 文献标志码:B 文章编码:1672-7274(2025)06-0050-04
Research on Network Security Threat Detection System Based on Big Data Analysis
ZHANGLianlian
(Chifeng Vocational College of Applied Technology,Chifeng O24oo5,China)
Abstract: With the increasing complexity of network security threats, traditional detection methods are facing challenges.This article proposes a network security threat detection system that combines big data analysis and deep learning.The system uses Apache Spark to process network traffcand log data,extract features,and improve the accuracyand real-time performance of network security threat detection through a combined model of LSTM and XGBoost.The experimental results show that compared to traditional SVM and decision tres, the proposed method has improved accuracy by 4.4% and recall rate by 10.7% (compared to SVM). Although there is a certain detection delay,it stillmeets the real-time detectionrequirements.Research has shown thatcombining big data and dep learning models can efectively improve network security threat detection capabilities and provide new ideas for network security protection.
Keywords:security threat detection;deep learning;bigdataanalysis
1 研究背景
随着网络攻击技术不断升级,传统安全措施难以应对复杂的网络威胁。(剩余4487字)