Due to a large amount of data and high dimension in currently network intrusion detection, a Moth-Flame Optimization (MFO) algorithm was applied to the feature selection of network intrusion detection. Since MFO algorithm converges fast and easy falls into local optimum, a Binary Moth-Flame Optimization integrated with Particle Swarm Optimization (BPMFO) algorithm was proposed. On one side, the spiral flight formula of the MFO algorithm was introduced to obtain strong local search ability. On the other side, the speed updating formula of the Particle Swarm Optimization (PSO) algorithm was combined to make the individual to move in the direction of global optimal solution and historical optimal solution, in order to increase the global convergence and avoid to fall into local optimum. By adopting KDD CUP 99 data set as the experimental basis, using three classifiers of Support Vector Machine (SVM), K-Nearest Neighbor ( KNN) and Naive Bayesian Classifier (NBC), Binary Moth-Flame Optimization (BMFO), Binary Particle Swarm Optimization (BPSO), Binary Genetic Algorithm (BGA), Binary Grey Wolf Optimization (BGWO) and Binary Cuckoo Search (BCS) were compared in the experiment. The experimental results show that, BPMFO algorithm has obvious advantages in the comprehensive performance including algorithm accuracy, operation efficiency, stability, convergence speed and jumping out of local optima when it is applied to the feature selection of network intrusion detection.