A multiple objective optimization based echo state network tree and application to intrusion detection

Echo state network tree (ESNTree) is proposed in this paper. ESNTree changes the activation function of the hidden layer and modifies the initialization method of echo state network (ESN). If the number of input features is too large, genetic algorithm is used to extract better features. Thus the complexity of input feature space can be reduced. On the other hand, a divide-and-conquer method is used. Decision tree and ESN are combined to decrease the complexity of the classifier and make the classifier more comprehensible and more interpretable. Experiments show that ESNTree achieves better performance than neural network tree (NNTree). ESNTree has been applied to intrusion detection successfully.