Intrusion Detection Based on PCA and Feature-weighted Fuzzy Clustering
Hu Lu · Journal of Jiangsu University of Science and Technology · 2008
In order to overcome the shortcomings that lots of redundancy information exists in intrusion detection data sets and classical clustering algorithms perform not perfectly,a new intrusion detection approach which combines the principle component analysis with feature-weighted fuzzy clustering is presented.The approach is splited into two steps,including feature extraction and fuzzy clustering.The principle component analysis is used to extract features and eliminate the redundancy attributes.The contribution proportion obtained from the former is used as the feature weight in the clustering algorithm,which forms the feature-weighted fuzzy clustering.Experiments on the data sets of KDDCUP99 show that this algorithm can obviously reduce the training time and meanwhile improve the accuracy of intrusion detection.