Mi, A.Z. and Hai, L.P. (2010) A Clustering-Based Classifier Selection Method for Network Intrusion Detection. 2010 5th International Conference on Computer Science and Education (ICCSE), Hefei, 24-27 August 2010,1001-1004.
http://dx.doi.org/10.1109/ICCSE.2010.5593398

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.