Study and Implementation of an Intrusion Detection System Based on Ensemble Learning in Neural Networks
Chang Wei-dong, Zhenghua Wang · Jisuanji fangzhen · 2007
In order to solve the problem of low detection rate for novel attacks and the difficulties in detecting un- known intrusions existing in traditional intrusion systems,the paper conducts the research and discussion of the en- semble learning,proposes a model based on ensemble learning in neural networks using genetic algorithm,and elab- orates the principle of the system and the main function of its various modules.This model selects a group of neural networks using genetic algorithm.Experiments show that using the ensemble learning method,the detection rate is higher than that of using any individual networks.At the same time,by using the machine learning method,this mod- el is adapted to the environment dynamically,so it has a better detection rate not only to the known intrusion,but also to the unknown intrusion,thus realizing an intelligent intrusion detection system.