摘要
本文针对当前在石油化工集成系统中,现行网络数据检测方法错误率较高、检测效率较低问题,提出了优化原有网络数据检测方法。基于网络时空性原理,借助多模数据流在网络中的相关性,使用滑动窗口模型与多模态数据融合,进而准确识别异常数据,在此基础上,利用常态化的随机变量对异常源进行身份认证,实现石化一体化信息系统中的网络数据安全性检验。以期本文所研究的内容能够给予相关人士一些参考和借鉴。
Aiming at the problems of high error rate and low detection efficiency of current network data detection methods in petrochemical integrated systems,this paper puts forward the optimization of original network data detection methods.On the basis of the principle of network spa-tiotemporal nature,combined with the correlation of multi-mode data flow in the network,the sliding window model and multi-mode data are used to realize the identification of abnormal data in the network,verify the abnormal data source according to the normally distributed random variables,and complete the security detection of the network data of the petrochemical integrated system.In order to study the content of this paper can give the rel-evant people some reference and reference.
作者
赵亮
Zhao Liang(Yunnan Petroleum Branch of China Petroleum and Chemical Corporation,Yunnan,650011)
出处
《当代化工研究》
CAS
2023年第22期134-136,共3页
Modern Chemical Research
关键词
石油化工集成系统
网络数据
数据安全
异常数据检测
petrochemical integrated system
network data
data security
abnormal data detection
