结合大数据分析与神经网络的互联网异常入侵行为实时检测  

RealTime Detection of Internet Abnormal Intrusion Behavior Using Big Data Analysis and Neural Networks

作　　者：田晨 TIAN Chen(China Mobile Communications Corporation Shandong Co.,Ltd.Ji nan City,Shandong Province 270000)

机构地区：[1]中国移动通信集团山东有限公司,山东济南270000

出　　处：《长江信息通信》2024年第6期16-18,共3页Changjiang Information & Communications

摘　　要：为了提升网络实时异常入侵行为检测效率和正确率,提出结合大数据分析与神经网络的互联网异常入侵行为实时检测方法。通过长短期记忆的方法构建入侵行为检测模型,并掌握网络实时异常入侵行为的特征,利用大数据分析技术的数据采集和处理实现网络实时异常入侵行为检测。通过仿真对比测试可知,模型对异常入侵行为检测结果的正确率以及检验效率高,并且耗时少,与其他的模型相比有较高的优越性。In order to improve the efficiency and accuracy of real-time abnormal intrusion behav-ior detection,a real-time detection method combining big data analysis and neural network is proposed.The intrusion bchavior detcction model is constructed by the method of long and short term memory,and the characteristics of real-time abnormal intrusion behavior are mastered.The real-time abnormal intrusion bchavior detection is realized by data acquisition and processing of big data analysis technology.Through the simulation and comparison test,it can be seen that the model has high accuracy and efficiency of abnormal intrusion detection results,and less time,and has higher advantages compared with other models.

关 键 词：大数据分析技术 通信网络 实时异常入侵行为检测 长短期记忆模型 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]