Performance Comparison of Features Reduction Techniques for Intrusion Detection System
Rupali Datti, Shilpa Lakhina · 2012
The network traffic data provided for the design of intrusion detection system always are large with ineffective information, thus we need to remove the worthless information from the original high dimensional database. In this paper, we compare the performance of two features reduction techniques on NSL-KDD dataset, which is now publicly available for the evaluation of Intrusion Detection System. These feature reduction techniques include Principal Component Analysis, Linear Discriminant Analysis. After reduction Error Back-Propagation Algorithm is used for classification. Our results shows that PCA performs better than LDA with small data set and with large data set LDA is superior than PCA.