基于最大信息传递熵的ICS因果关系建模

针对传统因果关系算法难以准确分析含大量噪声的非线性数据的问题进行了研究,提出基于最大信息传递熵的因果关系建模算法。首先,利用最大信息系数对非线性数据的时序趋势间的关联度进行检测,弱化噪声对变量间相关性的影响;然后根据筛选因子剔除弱相关变量,并通过随机经验估值计算强关联变量间的传递熵,以减少传递熵的计算量;最后,传递熵确定因果关系方向,形成支持链路溯源的单向因果网络。利用经典化工过程数据集对该算法进行测试分析,实验结果表明,相比于现有的因果关系建模算法,该算法可定位异常变量,对12维以上的高维数据建模的稳定性高于85%,因果关系的准确率可达83.33%,实际建模效果优于对比算法,可用于工业控制系统异常检测定位。

ICS causality modeling based on maximum information transfer entropy

This paper developed a causality modeling algorithm based on maximum information transfer entropy to solve the problem that traditional causality algorithms were difficult to accurately analyze non-linear data with a lot of noise.Firstly,it used the maximum information coefficient to detect the correlation between time series trends of non-linear data,and weaken the effect of noise on the correlation between variables.Secondly,it eliminated weakly related variables based on screening factors,and calculated the transfer entropy between strong correlations using stochastic empirical valuation thereby reducing the calculation amount of transfer entropy.Finally,it transferred entropy determined causal direction,formed a one-way causal network that supported link traceability.It tested analysis of the algorithm using classic chemical process data sets.Test results show that,compared with existing algorithms,this algorithm can locate abnormal variables.The stability of this algorithm for modeling high-dimensional data of more than 12 dimensions is higher than 85%,and the accuracy rate of causality can reach 83.33%.The actual modeling effect of this algorithm is better than the comparison algorithms,and it can detect and locate industrial control system abnormalities.