摘要：In order to improve the effectiveness of supervised self-organizing map (SSOM) neural network, a kind of genetic algorithm is designed to optimize it. To improve its classification rate, a real number encoding genetic algorithm is provided and used to optimize the learning rate and neighbor radius of SSOM. To speed up the modeling speed, a binary encoding genetic algorithm is provided to optimize input variables of SSOM and reduce its dimension of input sample. Finally, intrusion detection data set KDD Cup 1999 is used to carry out experiment based on the proposed model. The results show that the optimized model has shorter modeling time and higher intrusion detection rate.
