To solve the problem of the low detection accuracy of new intrusions with long detection time due to the complex and changeable nature of network intrusions

this paper proposes a network intrusion detection method based on the selective learning of Kernel Extreme Learning Machines (KELMs).First

based on the high efficiency learning characteristics of the single KELM learner

multiple KELMs are trained independently by the Bagging strategy.Then

based on the margin distance minimization (MDM) guidelines

KELM learners are integrated by selecting a part of them with high gains based on the MDM-based gain measures.Extensive validation and comparative experiments on the the KDD99 data set and on a hybrid network simulation platform mixed with wireless networks and Ethernet networks demonstrate that the proposed method achieves better recognition accuracies with faster recognition speed than the network intrusion detection methods based on the single learner and the traditional ensemble learning

which can effectively detect the known and unknown network intrusion connection in real time.