Ensemble Classifiers for Network Intrusion Detection System
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Zainal:2009:JIAS,
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author = "Anazida Zainal and Mohd Aizaini Maarof and
Siti Mariyam Shamsuddin",
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title = "Ensemble Classifiers for Network Intrusion Detection
System",
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journal = "Journal of Information Assurance and Security",
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year = "2009",
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volume = "4",
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number = "3",
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pages = "217--225",
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month = jun,
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note = "Special Issue on Intrusion and Malware Detection",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1554-1010",
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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:10.1.1.1044.3840",
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rights = "Metadata may be used without restrictions as long as
the oai identifier remains attached to it.",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1044.3840",
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URL = "https://static.aminer.org/pdf/PDF/000/346/803/intrusion_detection_in_computer_networks_by_multiple_classifier_systems.pdf",
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URL = "http://www.mirlabs.org/jias/secured/Volume4-Issue3/vol4-issue3.html",
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abstract = "Two of the major challenges in designing anomaly
intrusion detection are to maximise detection accuracy
and to minimise false alarm rate. In addressing this
issue, this paper proposes an ensemble of one-class
classifiers where each adopts different learning
paradigms. The techniques deployed in this ensemble
model are; Linear Genetic Programming (LGP), Adaptive
Neural Fuzzy Inference System (ANFIS) and Random Forest
(RF). The strengths from the individual models were
evaluated and ensemble rule was formulated. Prior to
classification, a 2-tier feature selection process was
performed to expedite the detection process. Empirical
results show an improvement in detection accuracy for
all classes of network traffic; Normal, Probe, DoS, U2R
and R2L. Random Forest, which is an ensemble learning
technique that generates many classification trees and
aggregates the individual result was also able to
address imbalance dataset problem that many of machine
learning techniques fail to sufficiently address it.",
- }
Genetic Programming entries for
Anazida Zainal
Mohd Aizaini Maarof
Siti Mariyam Shamsuddin
Citations