%0 Journal Article
%T Comparison of Feature Reduction Techniques for the Binominal Classification of Network Traffic
%A Adel Ammar
%J Journal of Data Analysis and Information Processing
%P 11-19
%@ 2327-7203
%D 2015
%I Scientific Research Publishing
%R 10.4236/jdaip.2015.32002
%X This paper tests various scenarios of feature selection and feature reduction, with the objective of building a real-time anomaly-based intrusion detection system. These scenarios are evaluated on the realistic Kyoto 2006+ dataset. The influence of reducing the number of features on the classification performance and the execution time is measured for each scenario. The so-called HVS feature selection technique detailed in this paper reveals many advantages in terms of consistency, classification performance and execution time.
%K Intrusion Detection
%K Network Security
%K Feature Selection
%K Kyoto Dataset
%K Neural Networks
%K PCA
%K PLS
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=56199