Research On Network Intrusion Detection Based On Clustering Analysis And Association Rules

As the continuous development of the network technology and the network applications, real-time and intelligence requirements become more and more important in intrusion detection system. Through data mining techniques can improve the processing power of network data,so it come impove to the real-time and intelligent requirements of the intrusion detection system. This article propose an intrusion etection systems based on cluster analysis and association rule.By using an improved method of weighted the membership of the network data, improved the way of update the cluster center of FCM clustering algorithm.So,it can cluster better, and reduce the number of iterations of clustering algorithms, also, reduces The time complexity of the FCM algorithm. Determined the number of clusters and initialcluster centers by use a simple clustering algorithms, it can avoid the FCM algorithm falling into local optimum, also can reduce the affect of clustering numbers and the initial cluster centers on the FCM algorithm.By using an improved Tree-storage structures improved the storage structures of transaction of the Apriori algorithm, reducing the times of scanning the database, and the uselessaffairs. Through using an improved Hash technique to generate 2-frequent item sets, it can reduce the time of generate 2-frequent item sets, thus reduce the time complexity of Apriori algorithm.By using an intrusion detection module based on improved FCM and association analysis, improved the way of correct the resulut of clustering by association rules,it can reduce the times of misjudging the clustering result by association rules.so it can improve detection rate of the intrusion detection system reduced its false detection rate.