Abstract: Differentiating the normal state and anomaly state is a difficult task in intrusion detection system,To solve the problem,an approach that applies Quantum-Behaved Partical Swarm Optimization(QPSO) to optimize parameters of membership functions in anomaly detection was presented.Parameters of membership functions were arranged into partical swarm,an optimal parameter-set could be derived by embedding fuzzy data mining in the process of evolution of partical,so normal state and anomaly state could be differentiated in the most extent,and the accuracy of anomaly detection was enhanced.Experiments prove the feasibility of the approach.