The intrusion data mining method for distributed network based on fuzzy kernel clustering algorithm

Abstract

In order to overcome the shortcomings of intrusion data mining method, which is easy to fall into local extremum and leads to poor mining effect, a distributed network intrusion data mining method based on fuzzy kernel clustering algorithm is studied. In this method, a new feature vector is established by using the Gauss kernel function which accords with Mercer condition through the fuzzy kernel clustering algorithm, so that the data pattern space can be effectively mapped to the high-dimensional feature space. When the new data does not meet the clustering condition, a new cluster set and sub box are established, until the end of all data clustering, the distributed network intrusion data mining is realised. The experimental results show that the accuracy of mining is higher than 95%, the false alarm rate and the false alarm rate are lower than 2%, and the total mining time is only 446 ms.