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.

摘要： 在入侵检测系统中，将正常状态与异常状态区分开来，是目前所面临的一个难点。针对这一问题，提出了在异常检测中运用量子粒子群算法（QPSO）对隶属度函数参数进行优化的方法。把隶属度函数里的参数组合当作一个粒子，在粒子的迭代进化中运用模糊数据挖掘的技术，可以搜索到最佳的参数组合，最大限度的把正常状态和异常状态区分开来，提高了异常检测的准确性，并通过实验验证了这一方法的可行性。