Evolutionary Methods for the Construction of Cryptographic Boolean Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Stjepan Picek and Domagoj Jakobovic and 
                 Julian F. Miller and Elena Marchiori and Lejla Batina",
  title =        "Evolutionary Methods for the Construction of
                 Cryptographic {Boolean} Functions",
  booktitle =    "18th European Conference on Genetic Programming",
  year =         "2015",
  editor =       "Penousal Machado and Malcolm I. Heywood and 
                 James McDermott and Mauro Castelli and 
                 Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
  series =       "LNCS",
  volume =       "9025",
  publisher =    "Springer",
  pages =        "192--204",
  address =      "Copenhagen",
  month =        "8-10 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, Boolean functions, Cryptographic
                 properties, Comparison: Poster",
  isbn13 =       "978-3-319-16500-4",
  DOI =          "doi:10.1007/978-3-319-16501-1_16",
  abstract =     "Boolean functions represent an important primitive
                 when constructing many stream ciphers. Since they are
                 often the only nonlinear element of such ciphers,
                 without them the algorithm would be trivial to break.
                 Therefore, it is not surprising there exist a
                 substantial body of work on the methods of constructing
                 Boolean functions. Among those methods, evolutionary
                 computation (EC) techniques play a significant role.
                 Previous works show it is possible to use EC methods to
                 generate high-quality Boolean functions that even
                 surpass those built by algebraic constructions.
                 However, up to now, there was no work investigating the
                 use of Cartesian Genetic Programming (CGP) for
                 producing Boolean functions suitable for cryptography.
                 In this paper we compare Genetic Programming (GP) and
                 CGP algorithms in order to reach the conclusion which
                 algorithm is better suited to evolve Boolean functions
                 suitable for cryptographic usage. Our experiments show
                 that CGP performs much better than the GP when the goal
                 is obtaining as high as possible nonlinearity. Our
                 results indicate that CGP should be further tested with
                 different fitness objectives in order to check the
                 boundaries of its performance.",
  notes =        "Part of \cite{Machado:2015:GP} EuroGP'2015 held in
                 conjunction with EvoCOP2015, EvoMusArt2015 and

Genetic Programming entries for Stjepan Picek Domagoj Jakobovic Julian F Miller Elena Marchiori Lejla Batina