JDAIP  Vol.3 No.2 , May 2015
Comparison of Feature Reduction Techniques for the Binominal Classification of Network Traffic
Author(s) Adel Ammar
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.

Cite this paper
Ammar, A. (2015) Comparison of Feature Reduction Techniques for the Binominal Classification of Network Traffic. Journal of Data Analysis and Information Processing, 3, 11-19. doi: 10.4236/jdaip.2015.32002.
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