Abstract: In order to improve the detection rate for anomaly state and reduce the false positive rate for normal state in the network anomaly detection，a novel method of network anomaly detection based on constructing Fuzzy Neural Network（FNN） using Particle Swarm Optimization（PSO） algorithm is proposed.The FNN is trained by the hybrid algorithm which is based on PSO and gradient descent.The model makes full use of the global optimization of PSO and local accurate searching of BP.The well-known KDD cup 1999 intrusion detection data set is used as the experimental data.Experimental result on KDD 99 intrusion detection datasets shows that this learning algorithm has more rapid convergence，better global convergence ability compared with the traditional Gradient Descent（GD） algorithm，and the accuracy of anomaly detection is enhanced.It also shows the remarkable ability of this novel algorithm to detect new type of attacks.