Ensemble Learning Based Intrusion Detection Method
基于集成学习的入侵检测方法

In order to solve the problem of low detection rate for novel attacks and the difficulties in detecting unknown intrusions existing in traditional intrusion systems, the paper proposed a model based on ensemble learning in improved BP neural networks and support vector machines. Experiments show that using the ensemble learning method, the detection rate is higher than that of using any individual networks and svm. So it has a better detection rate not only to the known intrusion, but also to the unknown intrusion.