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In order to effectively extract features from the malicious data collected to analyze, security network system security and stability, the need for network intrusion detection model is studied. But the current approach is to use genetic algorithm to find out the characteristics of the network intrusion subset of recycled for further selection of particle swarm optimization (pso), find out the optimal feature subset, finally using extreme learning machine classifying network intrusion, but this method has the problem of accuracy is low. Therefore, proposes a network intrusion detection methods based on feature selection. This method firstly in order to enhance optimal performance as the goal to feature selection of network intrusion detection, combined with analysis of characteristics of feature selection using the attributes of the Fisher than feature subset evaluation function is constructed, and combining with the feature subset of calculated results of evaluation function for support vector machine (SVM) to network intrusion detection based on feature selection methods. Simulation experiments show that support vector machine (SVM) is used to analyse the network intrusion detection can effectively improve the accuracy of the speed of intrusion detection and intrusion detection.