Ensemble Classifiers for Network Intrusion Detection System

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Zainal:2009:JIAS,

• author = "Anazida Zainal and Mohd Aizaini Maarof and Siti Mariyam Shamsuddin",
• title = "Ensemble Classifiers for Network Intrusion Detection System",
• journal = "Journal of Information Assurance and Security",
• year = "2009",
• volume = "4",
• number = "3",
• pages = "217--225",
• month = jun,
• note = "Special Issue on Intrusion and Malware Detection",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1554-1010",
• annote = "The Pennsylvania State University CiteSeerX Archives",
• bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
• language = "en",
• oai = "oai:CiteSeerX.psu:10.1.1.1044.3840",
• rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1044.3840",
• URL = "https://static.aminer.org/pdf/PDF/000/346/803/intrusion_detection_in_computer_networks_by_multiple_classifier_systems.pdf",
• URL = "http://www.mirlabs.org/jias/secured/Volume4-Issue3/vol4-issue3.html",
• 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