Application of Optimized KFCM Algorithm in Intrusion Detection Based on Non-Euclidean Distance
LI Cong
WANG Yun
HU Wenjun
DING Yong
Abstract：Based on FCM and by introducing the kernel function ,Kernel fuzzy C‐means clustering (KFCM ) make the sample points nonlinear mapped to a high‐dimensional feature space for clustering which can solve the problem of high dimen‐sional data space clustering .Like the classic FCM clustering algorithm and its Derived algorithm ,KFCM clustering algorithm is sensitive to noises or outliers .Based on modified objective function by using the robust statistical view ,a new non‐Euclide‐an distance is introduced to replace the Euclidean distance which can improve anti‐jamming capability of noise or outliers data . The improved algorithm is used to construct the model of intrusion detection system .Our experimental results show the pro‐posed algorithm can solve poor stability and low detection accuracy of the traditional clustering algorithms in intrusion detec‐tion .
Keywords：KFCMnon-Euclidean distanceintrusion detection
Publication Date：2014-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 2235-2238,2340 )