Securing Network Traffic Using Genetically Evolved Transformations

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

@Article{Faraoun:2006:MJCS,
  author =       "Kamel Mohamed Faraoun and Aoued Boukelif",
  title =        "Securing Network Traffic Using Genetically Evolved
                 Transformations",
  journal =      "Malaysian Journal of Computer Science",
  year =         "2006",
  volume =       "19",
  number =       "1",
  pages =        "9--28",
  keywords =     "genetic algorithms, genetic programming, patterns
                 classification, intrusion detection",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.531.8679",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.531.8679",
  URL =          "http://mjcs.fsktm.um.edu.my/document.aspx?FileName=349.pdf",
  URL =          "http://e-journal.um.edu.my/public/article-view.php?id=1026",
  size =         "20 pages",
  abstract =     "The paper describes a new approach of classification
                 using genetic programming. The proposed technique
                 consists of genetically coevolving a population of
                 non-linear transformations on the input data to be
                 classified, and map them to a new space with a reduced
                 dimension, in order to get maximum inter-classes
                 discrimination. The classification of new samples is
                 then performed on the transformed data, and so becomes
                 much easier. Contrary to the existing GP-classification
                 techniques, the proposed one uses a dynamic repartition
                 of the transformed data in separated intervals, the
                 efficacy of a given interval repartition is handled by
                 the fitness criterion, with maximum classes
                 discrimination. Experiments were first performed using
                 the Fisher's Iris dataset, and the KDD?99 Cup dataset
                 was used to study the intrusion detection and
                 classification problem. Obtained results demonstrate
                 that the proposed genetic approach outperforms the
                 existing GP-classification methods, and gives accepted
                 results compared to other existing techniques.",
}

Genetic Programming entries for Kamel Mohamed Faraoun Aoued Boukelif

Citations