模具技术Issue(2)：94-99,6.
基于大数据的模具制造网络流量异常检测与防御机制
Big data based network traffic anomaly detection and defense mechanism for mold manufacturing
摘要
Abstract
This paper focuses on the network security challenges in the digital transformation of mold manufacturing industry,and explores the network traffic anomaly detection and defense strategies based on big data technology.By integrating advanced technologies such as big data processing,machine learning,deep learning and generating countermeasure network(GAN),a comprehensive and intelligent network monitoring and protection system is constructed.This paper expounds the system framework from data collection to processing,analysis,detection and defense,and uses GAN technology to strengthen data and conduct confrontation training,thus improving the accuracy of anomaly detection and the intelligence of defense.Compared with the traditional support vector machine(SVM)algorithm,our method shows significant advantages in accuracy,recall,F1 score and detection ability of various attacks.This big data-based network traffic anomaly detection and defense scheme for mold manufacturing provides a practical and effective guarantee for the network security of the industry.关键词
大数据/模具制造/网络流量/异常检测/防御机制Key words
big data/mold manufacturing/network traffic/anomaly detection/defense mechanism分类
矿业与冶金引用本文复制引用
刘晶,李国朋,韩鹍..基于大数据的模具制造网络流量异常检测与防御机制[J].模具技术,2025,(2):94-99,6.基金项目
陕西省自然科学基础研究计划(编号:2022JM-395). （编号:2022JM-395）
