Securing Network Traffic Using Genetically Evolved Transformations

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@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