On the application of symbolic regression and genetic programming for cryptanalysis of symmetric encryption algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Smetka:2016:ICCST,
  author =       "Tomas Smetka and Ivan Homoliak and Petr Hanacek",
  booktitle =    "2016 IEEE International Carnahan Conference on
                 Security Technology (ICCST)",
  title =        "On the application of symbolic regression and genetic
                 programming for cryptanalysis of symmetric encryption
                 algorithm",
  year =         "2016",
  abstract =     "The aim of the paper is to show different point of
                 view on the problem of cryptanalysis of symmetric
                 encryption algorithms. Our dissimilar approach,
                 compared to the existing methods, lies in the use of
                 the power of evolutionary principles which are in our
                 cryptanalytic system applied with leveraging of the
                 genetic programming (GP) in order to perform known
                 plain text attack (KPA). Our expected result is to find
                 a program (i.e. function) that models the behaviour of
                 a symmetric encryption algorithm DES instantiated by
                 specific key. If such a program would exist, then it
                 could be possible to decipher new messages that have
                 been encrypted by unknown secret key. The GP is
                 employed as the basis of this work. GP is an
                 evolutionary algorithm-based methodology inspired by
                 biological evolution which is capable of creating
                 computer programs solving a corresponding problem. The
                 symbolic regression (SR) method is employed as the
                 application of GP in practical problem. The SR method
                 builds functions from predefined set of terminal blocks
                 in the process of the GP evolution; and these functions
                 approximate a list of input value pairs. The evolution
                 of GP is controlled by a fitness function which
                 evaluates the goal of a corresponding problem. The
                 Hamming distance, a difference between a current
                 individual value and a reference one, is chosen as the
                 fitness function for our cryptanalysis problem. The
                 results of our experiments did not confirmed initial
                 expectation. The number of encryption rounds did not
                 influence the quality of the best individual, however,
                 its quality was influenced by the cardinality of a
                 training set. The elimination of the initial and final
                 permutations had no influence on the quality of the
                 results in the process of evolution. These results
                 showed that our KPA GP solution is not capable of
                 revealing internal structure of the DES algorithm's
                 behaviour.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CCST.2016.7815720",
  month =        oct,
  notes =        "Also known as \cite{7815720}",
}

Genetic Programming entries for Tomas Smetka Ivan Homoliak Petr Hanacek

Citations