Clustering Intrusion Detection Model Based on Grey Fuzzy K mean Clustering

K means clustering algorithm is applied to the field of intrusion detection，it has the following problems.First，cluster analysis does not consider the correlation degree of the data flow，so clustering accuracy is not high；the second is prone to packet loss omissions because of data overload.In order to improve the clustering accuracy，this paper introduces grey analysis algorithm to improve the clustering accuracy.Meanwhile，in order to avoid the packet loss and underreporting phenomenon，we introduce clustering technology into the intrusion detection system to process the load balancing of the data stream，thereby overcome the contradiction between the high speed network data flow and low speed intrusion detection system processing capabilities.After a comprehensive analysis，this paper proposed an intrusion detection model based on the gray K means clustering algorithm.