Publication Details
On the Application of Symbolic Regression and Genetic Programming for Cryptanalysis of Symmetric Encryption Algorithm
symbolic regression, genetic programming, cryptanalysis, DES
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 utilized with utilization 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 behavior 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 values 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
functionality of our GP solution is verified by validation tests composed of
polynomials of various degrees. Control statements IF and FOR are verified by
computation of factorial function.The set of preconditions is determined in the
experimenting stage: estimation of the worst fitness value; finding the most
suitable GP parameters; transformation of KPA with elimination of an initial and
final permutations; evolution of the best individual; influence of the number of
encryption rounds; the cardinality of a training set; and the model
generalization.The results of the experiment did not approve the most of initial
assumptions. 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 behavior. The symbolic regression method proved
itself to be successful only within the convergence of the best solution where it
reveals up to the 70% of secret information (45 bits), however, sub-optimal
solutions do not need to be similar to optimal one.The complexity of the DES
algorithm encountered with the scalability of GP. The DES algorithm takes as
input a key containing 56 bits implying extensive state space explosion of
generated functions, in which the discovery of the best model is highly
improbable with contemporary technical capabilities.
@inproceedings{BUT134046,
author="Tomáš {Smetka} and Ivan {Homoliak} and Petr {Hanáček}",
title="On the Application of Symbolic Regression and Genetic Programming for Cryptanalysis of Symmetric Encryption Algorithm",
booktitle="Proceedings of 2016 IEEE International Carnahan Conference on Security Technology",
year="2016",
pages="305--312",
publisher="Institute of Electrical and Electronics Engineers",
address="Orlando, Fl",
doi="10.1109/CCST.2016.7815720",
isbn="978-1-5090-1072-1",
url="https://www.fit.vut.cz/research/publication/11144/"
}