Fusion of PCA and LDA for Intrusion Detection
Yong Wang · Computer Technology and Development · 2009
To solve the difficulty of feature extraction and the low performance in single IDS,an intrusion detection method based on the fusion of principal component analysis(PCA)and liner discriminate analysis(LDA)is presented.Firstly,PCA and LDA is applied to network intrusion feature extraction.Then,initial intrusion detection result is done by two KNN classifiers.Next,the D-S evidence theory is adopted to fuse these results for two classifiers can overcome the shortcomings of each other.Experiment has been done on dataset in KDD-99 and the results show that the performance of the proposed method is superior to that of the single classifier.