期刊论文

Distributed Network Information False Data Accurate Identification Simulation

中文

计算机仿真

1006-9348

2019

36

4

269-272

10.3969/j.issn.1006-9348.2019.04.056

本校为第一机构

对分布式网络信息虚假数据进行准确识别,能够降低用户数据发生篡改的风险,避免由虚假数据攻击导致的用户损失。当前虚假数据识别方法受网络噪声和数据报文丢失率的影响严重,导致检测识别的准确性不佳,由此,提出了基于分布式空间状态模型结合扩展卡尔曼的虚假数据识别方法。该方法首先利用邻近节点数据,对相邻节点的用户数据状态向量进行估计,确保计算的全局性;然后建立节点用户数据的空间状态模型方程,根据时间与观测值,利用扩展卡尔曼对用户数据进行估计更新,计算得到的估计值是其正常数据值的中心,通过判断观测值是否在正常范围,识别出特征数据是否为虚假数据。通过合成数据进行仿真,证明了提...

Accurate identification of false data of distributed network information can reduce the risk of tampering with user data and avoid user losses caused by false data attacks.The current false data recognition method is affected by the network noise and the data loss rate.The accuracy of detection and identification is not good.Based on the distributed spatial state model,this paper proposes a new method to identify false data of Kalman.The method firstly used the data of adjacent nodes,estimated the user data state vectors of adjacent nodes to ensure the overall performance of the calculation.Th...