Intrusion Detection Method Based on Ensemble Deep Forests
摘要
In practical application,the Convolutional Neural Network(CNN)-based intrusion detection method has some problems,such as long training time,a large number of hyper parameters,and a large amount of required data.In order to reduce the complexity of training and improve the efficiency of intrusion detection,this paper proposes an detection method based on Ensemble Deep Forests(EDF).On the basis of analyzing the hidden layer structure of CNN and the Bagging integration strategy of ensemble learning,the method constructs a Random Forest(RF) layer.Then the features randomly selected by the RF input are trained in each layer.The output class vectors and feature vectors are spliced,and iterations are passed to the next layer.Training continues until the model converges.Experimental results on the NSL-KDD dataset show that compared with the CNN algorithm,the EDF algorithm can improve the convergence speed by more than 50% while ensuring the classification accuracy,which proves the efficiency and feasibility of the EDF algorithm.