An Intrusion Detection Method for Electric Power Information Network Based on Improved Minimum Enclosing Ball Vector Machine
Wang Yu-fe · Power System Technology · 2013
To reduce the detection error and shorten the detection time during the detection of intrusion into electric power information network, based on the improved minimum enclosing ball vector machine(MEBVM) a method to detect the intrusion is proposed. This method abstracts the intrusion detection into multi-classification problem, by means of improving the training and learning of the improved MEVBM by samples of historical data an intrusion detection model is obtained. The improved MEVBM decreases the detection time-consuming by minimum enclosing ball, and during the training the particle swarm optimization(PSO) algorithm is utilized to dynamically search the optimal training parameters of MEBVM to reduce the error of intrusion detection model. Finally, the results of experiments based on field data of electric power information network show that comparing with traditional intrusion detection methods, the results of intrusion detection by the proposed method possess higher detection accuracy and shorter detection time-consuming.