Great Deluge Algorithm Feature Selection for Network Intrusion Detection
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Othman:2013:jasa,
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author = "Zulaiha Ali Othman and Lew Mei Theng and
Suhaila Zainudin and Hafiz Mohd Sarim",
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title = "Great Deluge Algorithm Feature Selection for Network
Intrusion Detection",
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journal = "Journal of Applied Science and Agriculture",
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year = "2013",
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volume = "8",
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number = "4",
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pages = "322--330",
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month = sep,
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keywords = "genetic algorithms, genetic programming, IDS, great
deluge algorithm, feature selection, anomaly
detection",
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ISSN = "1816-9112",
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annote = "The Pennsylvania State University CiteSeerX Archives",
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bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
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language = "en",
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oai = "oai:CiteSeerX.psu:10.1.1.1044.6495",
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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=10.1.1.1044.6495",
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URL = "http://www.aensiweb.com/old/jasa/rjfh/2013/322-330.pdf",
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size = "9 pages",
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abstract = "Intrusion detection systems (IDSs) deal with large
amounts of data containing irrelevant and/or redundant
features. These features result in a slow training and
testing process, heavy computational resources, and low
detection accuracy. Features selection, therefore, is
an important issue in IDSs. A reduced features set
improves the system accuracy and speeds up the training
and testing process considerably. In this paper propose
a wrapper-based feature selection techniques by using
Great Deluge algorithm (GDA) as the search strategy to
specify a candidate subset for evaluation, as well as
using Support Vector Machine (SVM) as the classifier
technique. The experiments used four random datasets
collected from KDD-cup99. Each data set contains around
4000 records. The performance of the proposed technique
has been evaluated based on classification accuracy by
comparing with other feature selection techniques such
as Bees Algorithm (BA), Rough-DPSO, Rough, Linear
Genetic Programming (LGP), Support Vector Decision
Function Ranking (SVDF), and Multivariate Regression
Splines (MARS). The result shows that the feature
subset produced by GDA yield high classification
accuracy when compared with other techniques.",
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notes = "Data Mining & Optimization Group (DMO), Centre of
Artificial intelligence Technology (CAIT), Faculty
ofComputer Science, UniversitiKebangsaan Malaysia,
Selangor, 43600 Malaysia",
- }
Genetic Programming entries for
Zulaiha Ali Othman
Lew Mei Theng
Suhaila Zainudin
Hafiz Mohd Sarim
Citations