Data Reduction and Ensemble Classifiers in Intrusion Detection
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- @InProceedings{Zainal:2008:AICMS,
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author = "Anazida Zainal and Mohd Aizaini Maarof and
Siti Mariyam Shamsuddin",
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title = "Data Reduction and Ensemble Classifiers in Intrusion
Detection",
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booktitle = "Second Asia International Conference on Modeling
Simulation, AICMS 08",
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year = "2008",
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month = may,
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pages = "591--596",
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keywords = "genetic algorithms, genetic programming, data
reduction, ensemble classifiers, intrusion detection,
network connection, traffic monitoring mechanism,
unnecessary recognition minimization, computer network
management, data reduction, minimisation, monitoring,
pattern classification, telecommunication security,
telecommunication traffic",
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DOI = "doi:10.1109/AMS.2008.146",
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abstract = "Efficiency is one of the major issues in intrusion
detection. Inefficiency is often attributed to high
overhead and this is caused by several reasons. Among
them are continuous detection and the use of full
feature set to look for intrusive patterns in the
network packet. The purpose of this paper are; to
address the issue of continuous detection by
introducing traffic monitoring mechanism and a lengthy
detection process by selectively choose significant
features to represent a network connection. In traffic
monitoring, a new recognition paradigm is proposed in
which it minimizes unnecessary recognition. Therefore,
the purpose of traffic monitoring is two-folds; to
reduce amount of data to be recognized and to avoid
unnecessary recognition. Empirical results show 30 to
40 percent reduction of normal connections is achieved
in DARPA KDDCup 1999 datasets. Finally we assembled
Adaptive Neural Fuzzy Inference System and Linear
Genetic Programming to form an ensemble classifiers.
Classification results showed a small improvement using
the ensemble approach for DoS and R2L classes.",
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notes = "Also known as \cite{4530542}",
- }
Genetic Programming entries for
Anazida Zainal
Mohd Aizaini Maarof
Siti Mariyam Shamsuddin
Citations