Ensemble of One-Class Classifiers for Network Intrusion Detection System
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Zainal:2008:ISIAS,
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author = "Anazida Zainal and Mohd Aizaini Maarof and
Siti Mariyam Shamsuddin and Ajith Abraham",
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title = "Ensemble of One-Class Classifiers for Network
Intrusion Detection System",
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booktitle = "Fourth International Conference on Information
Assurance and Security, ISIAS '08",
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year = "2008",
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month = sep,
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pages = "180--185",
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keywords = "genetic algorithms, genetic programming, adaptive
neural fuzzy inference system, classification trees,
linear genetic programming, machine learning
techniques, network intrusion detection system, network
traffic, one-class classifiers, random forest, fuzzy
neural nets, fuzzy reasoning, learning (artificial
intelligence), linear programming, security of data",
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DOI = "doi:10.1109/IAS.2008.35",
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abstract = "To achieve high accuracy while lowering false alarm
rates are major challenges in designing an intrusion
detection system. In addressing this issue, this paper
proposes an ensemble of one-class classifiers where
each uses 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. Empirical results show an
improvement in detection accuracy for all classes of
network traffic; normal, probe, DoS, U2R and R2L. RF,
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.",
-
notes = "Also known as \cite{4627082}",
- }
Genetic Programming entries for
Anazida Zainal
Mohd Aizaini Maarof
Siti Mariyam Shamsuddin
Ajith Abraham
Citations