Grammatical Evolution based Data Mining for Network Intrusion Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@PhdThesis{Wilson:thesis,
  author =       "Dominic Wilson",
  title =        "Grammatical Evolution based Data Mining for Network
                 Intrusion Detection",
  school =       "Electrical Engineering and Computer Science,
                 University of Toledo",
  year =         "2008",
  address =      "Toledo, OH, USA",
  month =        "7 " # apr,
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  URL =          "http://search.proquest.com/docview/304437540",
  size =         "222 pages",
  abstract =     "Grammatical Evolution (GE) is an Evolutionary
                 Computing technique which can generate programs or
                 codes in various languages based on the choice of a
                 grammar. The evolutionary dynamics of GE is complicated
                 and not well understood. The current body of knowledge
                 on GE is largely based on empirical performance studies
                 on some applications. There is little theoretical
                 foundation or detailed analysis of evolutionary
                 dynamics for GE in the literature. The limited
                 knowledge on its mechanism is a limiting factor for
                 applying GE to real world problems. An important real
                 world application of data mining is the automated
                 generation of knowledge from network intrusion data.
                 Network intrusion detection systems are becoming a
                 standard security feature in network infrastructures.
                 Unfortunately current systems are not very good at
                 detecting new types of intrusion without an associated
                 high rate of false alarms. A goal of this research is
                 to investigate and evaluate the real world application
                 of data mining using GE, by assessing mechanisms for
                 building effective and efficient intrusion detection
                 systems based on GE. The methodology used involves
                 fundamental theoretical analysis of GE, detailed
                 analysis of its evolutionary dynamics and
                 experimentation of GE concepts in mining datasets. The
                 results include contributions to the body of scientific
                 knowledge in Evolutionary Computing, GE and Data
                 Mining.",
  notes =        "'As of July 2014 ProQuest is no longer offering the
                 Udini service'

                 Supervisor Dr. Devinder Kaur",
}

Genetic Programming entries for Dominic Wilson

Citations