Genetic Programming Approach for Multi-Category Pattern Classification Applied to Network Intrusions Detection
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{Faraoun:2006:IJCIA,
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author = "K. M. Faraoun and A. Boukelif",
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title = "Genetic Programming Approach for Multi-Category
Pattern Classification Applied to Network Intrusions
Detection",
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journal = "International Journal of Computational Intelligence
and Applications (IJCIA)",
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year = "2006",
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volume = "6",
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number = "1",
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pages = "77--100",
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month = mar,
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keywords = "genetic algorithms, genetic programming, patterns
classification, intrusion detection",
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ISSN = "1469-0268",
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URL = "http://direct.bl.uk/bld/PlaceOrder.do?UIN=193825360&ETOC=RN&from=searchengine",
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DOI = "doi:10.1142/S1469026806001812",
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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