hybrid feature selection algorithm for intrusion detection system
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{oai:doaj.org/article:2b97571506b643c1881e8a9bdb4636a6,
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author = "Seyed Reza Hasani and Zulaiha Ali Othman and
Seyed Mostafa Mousavi Kahaki",
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title = "hybrid feature selection algorithm for intrusion
detection system",
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journal = "Journal of Computer Science",
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publisher = "Science Publications",
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year = "2014",
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volume = "10",
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number = "6",
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pages = "1015--1025",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1549-3636",
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bibsource = "OAI-PMH server at doaj.org",
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language = "English",
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oai = "oai:doaj.org/article:2b97571506b643c1881e8a9bdb4636a6",
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URL = "http://www.thescipub.com/pdf/10.3844/jcssp.2014.1015.1025",
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DOI = "DOI:10.3844/jcssp.2014.1015.1025",
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size = "11 pages",
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abstract = "Network security is a serious global concern.
Usefulness Intrusion Detection Systems (IDS) are
increasing incredibly in Information Security research
using Soft computing techniques. In the previous
researches having irrelevant and redundant features are
recognised causes of increasing the processing speed of
evaluating the known intrusive patterns. In addition,
an efficient feature selection method eliminates
dimension of data and reduce redundancy and ambiguity
caused by none important attributes. Therefore, feature
selection methods are well-known methods to overcome
this problem. There are various approaches being used
in intrusion detections, they are able to perform their
method and relatively they are achieved with some
improvements. This work is based on the enhancement of
the highest Detection Rate (DR) algorithm which is
Linear Genetic Programming (LGP) reducing the False
Alarm Rate (FAR) incorporates with Bees Algorithm.
Finally, Support Vector Machine (SVM) is one of the
best candidate solutions to settle IDSs problems. In
this study four sample dataset containing 4000 random
records are excluded randomly from this dataset for
training and testing purposes. Experimental results
show that the LGP_BA method improves the accuracy and
efficiency compared with the previous related research
and the feature subcategory offered by LGP_BA gives a
superior representation of data.",
- }
Genetic Programming entries for
Seyed Reza Hasani
Zulaiha Ali Othman
Seyed Mostafa Mousavi Kahaki
Citations