Data Reduction and Ensemble Classifiers in Intrusion Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Anazida Zainal and Mohd Aizaini Maarof and 
                 Siti Mariyam Shamsuddin",
  title =        "Data Reduction and Ensemble Classifiers in Intrusion
  booktitle =    "Second Asia International Conference on Modeling
                 Simulation, AICMS 08",
  year =         "2008",
  month =        may,
  pages =        "591--596",
  keywords =     "genetic algorithms, genetic programming, data
                 reduction, ensemble classifiers, intrusion detection,
                 network connection, traffic monitoring mechanism,
                 unnecessary recognition minimization, computer network
                 management, data reduction, minimisation, monitoring,
                 pattern classification, telecommunication security,
                 telecommunication traffic",
  DOI =          "doi:10.1109/AMS.2008.146",
  abstract =     "Efficiency is one of the major issues in intrusion
                 detection. Inefficiency is often attributed to high
                 overhead and this is caused by several reasons. Among
                 them are continuous detection and the use of full
                 feature set to look for intrusive patterns in the
                 network packet. The purpose of this paper are; to
                 address the issue of continuous detection by
                 introducing traffic monitoring mechanism and a lengthy
                 detection process by selectively choose significant
                 features to represent a network connection. In traffic
                 monitoring, a new recognition paradigm is proposed in
                 which it minimizes unnecessary recognition. Therefore,
                 the purpose of traffic monitoring is two-folds; to
                 reduce amount of data to be recognized and to avoid
                 unnecessary recognition. Empirical results show 30 to
                 40 percent reduction of normal connections is achieved
                 in DARPA KDDCup 1999 datasets. Finally we assembled
                 Adaptive Neural Fuzzy Inference System and Linear
                 Genetic Programming to form an ensemble classifiers.
                 Classification results showed a small improvement using
                 the ensemble approach for DoS and R2L classes.",
  notes =        "Also known as \cite{4530542}",

Genetic Programming entries for Anazida Zainal Mohd Aizaini Maarof Siti Mariyam Shamsuddin