Online Intrusion Detection Mechanism Based on Model Migration in Intelligent Pumped Storage Power Stations

With the continuous integration of new energy into the power grid,various new attacks con-tinue to emerge and the feature distributions are con-stantly changing during the deployment of intelligent pumped storage power stations.The intrusion detec-tion model trained on the old data is hard to effec-tively identify new attacks,and it is difficult to update the intrusion detection model in time when lacking data.To solve this issue,by using model-based trans-fer learning methods,in this paper we propose a con-volutional neural network(CNN)based transfer on-line sequential extreme learning machine(TOS-ELM)scheme to enable the online intrusion detection,which is called CNN-TOSELM in this paper.In our proposed scheme,we use pre-trained CNN to extract the char-acteristics of the target domain data as input,and then build online learning classifier TOS-ELM to transfer the parameter of the ELM classifier of the source do-main.Experimental results show the proposed CNN-TOSELM scheme can achieve better detection perfor-mance and extremely short model update time for in-telligent pumped storage power stations.