基于大数据分析的网络实时异常入侵行为检测  

Real Time Anomaly Intrusion Behavior Detection in Networks Based on Big Data Analysis

作　　者：陈家乐 CHEN Jiale(Zhengzhou University of Industrial Technology,Zhengzhou Henan 451100,China)

机构地区：[1]郑州工业应用技术学院,河南郑州451100

出　　处：《信息与电脑》2024年第2期198-200,共3页Information & Computer

摘　　要：文章提出基于大数据分析的网络实时异常入侵行为检测方法,利用大数据分析技术中的支持向量机构建异常入侵行为检测模型,将网络信号作为输入,通过学习与训练输出网络异常入侵行为检测结果。实验结果表明,该方法能够有效实现网络异常入侵检测,辨别异常入侵行为类别。This paper proposes a real-time network anomaly intrusion detection method based on big data analysis.It uses support vector machines in big data analysis technology to build an anomaly intrusion detection model,takes network signals as input,and outputs network anomaly intrusion detection results through learning and training.The experimental results show that this method can effectively achieve network anomaly intrusion detection and distinguish the types of abnormal intrusion behaviors.

关 键 词：大数据分析 网络异常 入侵行为检测 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]