On the application of symbolic regression and genetic programming for cryptanalysis of symmetric encryption algorithm
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gp-bibliography.bib Revision:1.8110
- @InProceedings{Smetka:2016:ICCST,
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author = "Tomas Smetka and Ivan Homoliak and Petr Hanacek",
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booktitle = "2016 IEEE International Carnahan Conference on
Security Technology (ICCST)",
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title = "On the application of symbolic regression and genetic
programming for cryptanalysis of symmetric encryption
algorithm",
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year = "2016",
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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.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CCST.2016.7815720",
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month = oct,
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notes = "Also known as \cite{7815720}",
- }
Genetic Programming entries for
Tomas Smetka
Ivan Homoliak
Petr Hanacek
Citations