Ensemble of One-Class Classifiers for Network Intrusion Detection System

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Zainal:2008:ISIAS,
  author =       "Anazida Zainal and Mohd Aizaini Maarof and 
                 Siti Mariyam Shamsuddin and Ajith Abraham",
  title =        "Ensemble of One-Class Classifiers for Network
                 Intrusion Detection System",
  booktitle =    "Fourth International Conference on Information
                 Assurance and Security, ISIAS '08",
  year =         "2008",
  month =        sep,
  pages =        "180--185",
  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",
  DOI =          "doi:10.1109/IAS.2008.35",
  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