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Arauujo, N., de Oliveira, R., Ferreira, E.-W., Shinoda, A.A. and Bhargava, B. (2010) Identifying Important Characteristics in the KDD99 Intrusion Detection Dataset by Feature Selection Using a Hybrid Approach. 2010 IEEE 17th International Conference on Telecommunications (ICT), Doha, 4-7 April 2010, 552-558.
http://dx.doi.org/10.1109/ICTEL.2010.5478852

has been cited by the following article:

• 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.