基于KQPSO聚类算法的网络异常检测

Network anomaly detection based on KQPSO clustering algorithm

提出一种基于KQPSO聚类算法的网络异常检测模型.该模型利用K-Means聚类算法的结果重新初始化粒子群,聚类过程都是根据数据间的Euclidean(欧几里德)距离。再通过量子粒子群优化算法(QPSO)寻找聚类中心。最后进行仿真模拟,实验结果表明,该模型对网络异常检测是有效的。

Model of detecting network anomaly based on KQPSO（K-Means Quantum-behaved Particle Swarm Optimization） clustering algorithms is presented.The authors uses K-Means clustering to seed the initial swam.All the process of clustering is based on the Euclidean distance among data vector.Cluster-centroid is chosen by QPSO clustering algorithm.Finally,the experiment result shows that this model is effective for network anomaly detection.