Abstract:In order to obtain a more ideal network intrusion detection results, according to the network intrusion feature selection and sample selection problem, this paper proposes a network intrusion detection model based on features selecting and samples selecting. Firstly, the features of network intrusion are extracted, and normalized, and secondly kernel principal component analysis is used to select intrusion features, and the samples are selection, finally, extreme learning machine is used to set up network intrusion detection classifier, and the simulation experiments are carried out with KDD Cup99 data. The simulation results show that that the proposed model has been better network intrusion detection results, the detection rate is above 95%, the efficiency of intrusion detection can meet the requirements of network security protection.