Based on a Semi-supervised Fuzzy Clustering and Sample Selection Attribute Reduction of the Intrusion Detection

Abstract

In order to improve the detection of intrusion detection rate and lower false detection rate, and put forward a kind of attribute reduction based on a semi-supervised fuzzy clustering method, and applied to the intrusion detection, first of all select the samples of data preprocessing, use a semi-supervised fuzzy clustering to reduce sample, with the reduction algorithm based on attribute dependence. Finally, reduction was carried out on the sample set. Simulation experiments using KDD99 data set, the text results show that the detection has higher efficiency.