A Linear Genetic Programming Approach to Intrusion Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{song:2003:gecco,
  author =       "Dong Song and Malcolm I. Heywood and 
                 A. Nur Zincir-Heywood",
  title =        "A Linear Genetic Programming Approach to Intrusion
                 Detection",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2003",
  editor =       "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and 
                 D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and 
                 R. Standish and G. Kendall and S. Wilson and 
                 M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and 
                 A. C. Schultz and K. Dowsland and N. Jonoska and 
                 J. Miller",
  year =         "2003",
  pages =        "2325--2336",
  address =      "Chicago",
  publisher_address = "Berlin",
  month =        "12-16 " # jul,
  volume =       "2724",
  series =       "LNCS",
  ISBN =         "3-540-40603-4",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Real World
                 Applications",
  URL =          "http://users.cs.dal.ca/~mheywood/X-files/Publications/27242325.pdf",
  DOI =          "doi:10.1007/3-540-45110-2_125",
  abstract =     "Page-based Linear Genetic Programming (GP) is proposed
                 and implemented with two-layer Subset Selection to
                 address a two-class intrusion detection classification
                 problem as defined by the KDD-99 benchmark dataset. By
                 careful adjustment of the relationship between subset
                 layers, over fitting by individuals to specific subsets
                 is avoided. Moreover, efficient training on a dataset
                 of 500,000 patterns is demonstrated. Unlike the current
                 approaches to this benchmark, the learning algorithm is
                 also responsible for deriving useful temporal features.
                 Following evolution, decoding of a GP individual
                 demonstrates that the solution is unique and
                 comparative to hand coded solutions found by experts.",
  notes =        "GECCO-2003. A joint meeting of the twelfth
                 International Conference on Genetic Algorithms
                 (ICGA-2003) and the eighth Annual Genetic Programming
                 Conference (GP-2003)",
}

Genetic Programming entries for Dong Song Malcolm Heywood Nur Zincir-Heywood

Citations