earticle

영어

Electric power SCADA (Supervisory Control and Data Acquisition) system gradually transforming from a separate private network to an open public network, seriously increases the vulnerability risk in electric power SCADA. In order to assess the vulnerability risk in electric power SCADA system, the paper firstly uses Delphi method and AHP (Analytic Hierarchy Process) to build an index system of vulnerability risk assessment, to fully represent the vulnerability of electric power SCADA system. As index data of vulnerability risk assessment in power SCADA is characterized by strong relation and high dimensionality, the method of Autoencoder is proposed to reduce dimensionality of index data by representing high-dimensional data in a low dimensional space. Auto encoder method can obtain the optimal initial weight in pre-training and then back-propagate error derivatives adjusting weights with the initial weights to minimize the reconstruction error finally getting the best reconstructed results. The paper conducts simulation experiments about reconstruction error in pre-training and fine-tuning process in MATLAB experimental platform, and the experimental results show that dimensional code received by reducing dimensionality of data can basically fully represent high-dimensional data. The low-dimensional code as input can significantly reduce the complexity in the construction of model of vulnerability risk assessment in Electric power SCADA system in later work.