Title: The intrusion data mining method for distributed network based on fuzzy kernel clustering algorithm
Authors: Linlin Li
Addresses: School of Data Science and Technology, Heilongjiang University, Harbin, Heilongjiang, 150080, China
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.
Keywords: fuzzy kernel clustering; algorithm; distributed; network; intrusion; data mining.
International Journal of Autonomous and Adaptive Communications Systems, 2022 Vol.15 No.1, pp.32 - 45
Received: 15 Jan 2020
Accepted: 18 May 2020
Published online: 18 May 2022 *