A Linear Genetic Programming Approach to Intrusion Detection
Created by W.Langdon from
gp-bibliography.bib Revision:1.8120
- @InProceedings{song:2003:gecco,
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author = "Dong Song and Malcolm I. Heywood and
A. Nur Zincir-Heywood",
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title = "A Linear Genetic Programming Approach to Intrusion
Detection",
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booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
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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",
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year = "2003",
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pages = "2325--2336",
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address = "Chicago",
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publisher_address = "Berlin",
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month = "12-16 " # jul,
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volume = "2724",
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series = "LNCS",
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ISBN = "3-540-40603-4",
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publisher = "Springer-Verlag",
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keywords = "genetic algorithms, genetic programming, Real World
Applications",
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URL = "http://users.cs.dal.ca/~mheywood/X-files/Publications/27242325.pdf",
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DOI = "doi:10.1007/3-540-45110-2_125",
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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.",
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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