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Kayacik, H.G., Zincir-Heywood, A.N. and Heywood, M.I. (2005) Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets. Proceedings of the 3rd Annual Conference on Privacy, Security and Trust, 12-14 October 2005, 85-89.

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.