On the Fitness Functions Involved in Genetic Algorithms and the Cryptanalysis of Block Ciphers

Area of Science:

• Cryptography and Computer Science
• Algorithmic Cryptanalysis
• Computational Intelligence

Background:

• Genetic Algorithms (GAs) are increasingly applied in cryptanalysis, particularly for block ciphers.
• Recent research focuses on enhancing the properties and characteristics of GAs for cryptographic applications.
• The effectiveness of GA-based cryptanalysis hinges on the design of appropriate fitness functions.

Purpose of the Study:

• To investigate and analyze the fitness functions employed in Genetic Algorithms for cryptanalysis.
• To develop a theoretical framework for characterizing fitness functions in GA-based block cipher attacks.
• To establish criteria for a priori determination of the effectiveness of different fitness functions.

Main Methods:

• Proposed a methodology to validate the correlation between fitness function values (using decimal distance) and key proximity.
• Developed a theoretical foundation for characterizing the performance of various fitness functions.
• Focused on the application of Genetic Algorithms in the cryptanalysis of block ciphers.

Main Results:

• Demonstrated that closeness to 1 in certain decimal distance-based fitness functions implies decimal closeness to the cryptographic key.
• Established a theoretical basis for evaluating and comparing different fitness functions.
• Provided insights into predicting the efficacy of specific fitness functions in cryptanalytic attacks.

Conclusions:

• The study provides a theoretical and methodological framework for understanding and optimizing fitness functions in GA-based cryptanalysis.
• The findings enable a priori assessment of fitness function effectiveness, guiding the selection of superior methods for block cipher attacks.
• This research contributes to advancing the application of Genetic Algorithms in the field of cryptography.