An efficient feature reduction technique for intrusion detection system
Shailendra Narayan Singh, Sanjay Silakari, Ravindra Patel · 2011
The information security is an issue of serious global concern. 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. To improve the generalization ability, we usually generate a small set of features from the original input variables by feature extraction. The conventional Principal Component Analysis (PCA) feature reduction technique has its limitations. It is not suitable for non-linear dataset. Thus we propose an efficient algorithm based on the Generalized Discriminant Analysis (GDA) feature reduction technique which is novel approach used in the area of intrusion detection. This not only reduces the number of the input features but also increases the classification accuracy and reduces the training time of the classifiers by selecting most discriminating features. We use Self-Organizing Map (SOM) and C4.5 classifiers to compare the performance of the proposed technique. The result indicates the superiority of GDA.