TITLE:
Comparison of Feature Reduction Techniques for the Binominal Classification of Network Traffic
AUTHORS:
Adel Ammar
KEYWORDS:
Intrusion Detection, Network Security, Feature Selection, Kyoto Dataset, Neural Networks, PCA, PLS
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.3 No.2,
May
8,
2015
ABSTRACT: 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.