Genetic Programming Approach for Multi-Category Pattern Classification Applied to Network Intrusions Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Faraoun:2006:IJCIA,
  author =       "K. M. Faraoun and A. Boukelif",
  title =        "Genetic Programming Approach for Multi-Category
                 Pattern Classification Applied to Network Intrusions
                 Detection",
  journal =      "International Journal of Computational Intelligence
                 and Applications (IJCIA)",
  year =         "2006",
  volume =       "6",
  number =       "1",
  pages =        "77--100",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, patterns
                 classification, intrusion detection",
  ISSN =         "1469-0268",
  URL =          "http://direct.bl.uk/bld/PlaceOrder.do?UIN=193825360&ETOC=RN&from=searchengine",
  DOI =          "doi:10.1142/S1469026806001812",
  abstract =     "The present paper describes a new approach of
                 classification using genetic programming. The proposed
                 technique consists of genetically co-evolve a
                 population of nonlinear transformations on the input
                 data to be classified, and map them to a new space with
                 reduced dimension in order to get a maximum
                 inter-classes discrimination. It is much easier to
                 classify the new samples from the transformed data.
                 Contrary to the existing GP-classification techniques,
                 the proposed one uses a dynamic repartition of the
                 transformed data in separated intervals, the efficiency
                 of a given intervals repartition is handled by the
                 fitness criterion, with a maximum classes
                 discrimination. Experiments were performed using the
                 Fisher's Iris dataset. After that, the KDD'99 Cup
                 dataset was used to study the intrusion detection and
                 classification problem. The results demonstrate that
                 the proposed genetic approach outperforms the existing
                 GP-classification methods, and provides improved
                 results compared to other existing techniques.",
}

Genetic Programming entries for Kamel Mohamed Faraoun Aoued Boukelif

Citations